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"name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 236.025, + "cuda_time_us": 37.792, + "pct_cuda_time": 0.06716154827912481, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rms_norm_kernel(c10::BFloat16*, c10::BFloat16 const*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.792, + "pct_cuda_time": 0.06716154827912481, + "trace": "_C::rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 3010.865, + "cuda_time_us": 388.02600000000007, + "pct_cuda_time": 0.6895752257767699, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 413.062, + "cuda_time_us": 172.73200000000003, + "pct_cuda_time": 0.3069683678384258, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0013061954377952142, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 171.997, + "pct_cuda_time": 0.3056621724006306, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 990.272, + "cuda_time_us": 33.824, + "pct_cuda_time": 0.06010987005168071, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.824, + "pct_cuda_time": 0.06010987005168071, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1059.999, + "cuda_time_us": 51.327000000000005, + "pct_cuda_time": 0.09121509283770743, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.96, + "pct_cuda_time": 0.023031690984797244, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 37.087, + "pct_cuda_time": 0.06590866694083145, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0022747349120787403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 297.3, + "cuda_time_us": 130.143, + "pct_cuda_time": 0.23128189504895585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 129.407, + "pct_cuda_time": 0.2299739224745106, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 116.996, + "cuda_time_us": 25.952, + "pct_cuda_time": 0.04612025034239647, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 25.952, + "pct_cuda_time": 0.04612025034239647, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 620.099, + "cuda_time_us": 1302.446, + "pct_cuda_time": 2.3146245213260213, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 190.164, + "cuda_time_us": 780.533, + "pct_cuda_time": 1.387113800882465, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 779.797, + "pct_cuda_time": 1.38580582830802, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 187.787, + "cuda_time_us": 109.727, + "pct_cuda_time": 0.19499987320129997, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.727, + "pct_cuda_time": 0.19499987320129997, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 174.709, + "cuda_time_us": 412.186, + "pct_cuda_time": 0.732510847242256, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 411.45, + "pct_cuda_time": 0.7312028746678106, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2612.939, + "cuda_time_us": 1738.3129999999999, + "pct_cuda_time": 3.0892197415783835, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.841, + "cuda_time_us": 26.528, + "pct_cuda_time": 0.04714388105283189, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.528, + "pct_cuda_time": 0.04714388105283189, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1848.632, + "cuda_time_us": 383.068, + "pct_cuda_time": 0.6807641822657647, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.239, + "cuda_time_us": 167.07, + "pct_cuda_time": 0.29690622012577744, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.334, + "pct_cuda_time": 0.2955982475513322, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 525.803, + "cuda_time_us": 34.112, + "pct_cuda_time": 0.060621685406898426, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.112, + "pct_cuda_time": 0.060621685406898426, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 790.825, + "cuda_time_us": 50.911, + "pct_cuda_time": 0.09047580399128184, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 13.152, + "pct_cuda_time": 0.023372901221609056, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.287, + "pct_cuda_time": 0.06448695762078221, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.472, + "pct_cuda_time": 0.002615945148890551, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 222.608, + "cuda_time_us": 130.975, + "pct_cuda_time": 0.23276047274180703, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 130.239, + "pct_cuda_time": 0.2314525001673618, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.811, + "cuda_time_us": 26.079, + "pct_cuda_time": 0.04634594669695427, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.079, + "pct_cuda_time": 0.04634594669695427, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 510.67, + "cuda_time_us": 1302.638, + "pct_cuda_time": 2.3149657315628325, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 167.046, + "cuda_time_us": 779.925, + "pct_cuda_time": 1.3860333017992277, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 779.189, + "pct_cuda_time": 1.3847253292247823, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 111.957, + "cuda_time_us": 109.887, + "pct_cuda_time": 0.1952842150653098, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.887, + "pct_cuda_time": 0.1952842150653098, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 178.509, + "cuda_time_us": 412.82599999999996, + "pct_cuda_time": 0.7336482146982953, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 412.058, + "pct_cuda_time": 0.732283373751048, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2588.022, + "cuda_time_us": 1745.993, + "pct_cuda_time": 3.1028681510508562, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.25, + "cuda_time_us": 26.08, + "pct_cuda_time": 0.04634772383360433, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.08, + "pct_cuda_time": 0.04634772383360433, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1874.174, + "cuda_time_us": 384.38, + "pct_cuda_time": 0.6830957855506454, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 147.027, + "cuda_time_us": 168.31799999999998, + "pct_cuda_time": 0.2991240866650542, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 167.582, + "pct_cuda_time": 0.29781611409060893, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 544.543, + "cuda_time_us": 33.823, + "pct_cuda_time": 0.06010809291503065, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.823, + "pct_cuda_time": 0.06010809291503065, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 801.661, + "cuda_time_us": 51.678999999999995, + "pct_cuda_time": 0.09184064493852907, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.895, + "pct_cuda_time": 0.022916177102543248, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 37.312, + "pct_cuda_time": 0.06630852268709528, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.472, + "pct_cuda_time": 0.002615945148890551, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 207.489, + "cuda_time_us": 130.56, + "pct_cuda_time": 0.23202296103203152, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.769, + "pct_cuda_time": 0.0013666180838973058, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 129.791, + "pct_cuda_time": 0.23065634294813422, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.686, + "cuda_time_us": 27.359, + "pct_cuda_time": 0.04862068160903302, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 27.359, + "pct_cuda_time": 0.04862068160903302, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 468.68, + "cuda_time_us": 1308.174, + "pct_cuda_time": 2.324803960057573, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 166.93, + "cuda_time_us": 785.365, + "pct_cuda_time": 1.3957009251755625, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 784.629, + "pct_cuda_time": 1.3943929526011172, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.55, + "cuda_time_us": 109.311, + "pct_cuda_time": 0.19426058435487437, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.311, + "pct_cuda_time": 0.19426058435487437, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.305, + "cuda_time_us": 413.498, + "pct_cuda_time": 0.7348424505271367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 412.73, + "pct_cuda_time": 0.7334776095798894, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 5463.612, + "cuda_time_us": 1747.239, + "pct_cuda_time": 3.105082463316833, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.726, + "cuda_time_us": 26.496, + "pct_cuda_time": 0.047087012680029926, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.496, + "pct_cuda_time": 0.047087012680029926, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1826.112, + "cuda_time_us": 382.874, + "pct_cuda_time": 0.6804194177556528, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.904, + "cuda_time_us": 167.869, + "pct_cuda_time": 0.29832615230917664, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 167.101, + "pct_cuda_time": 0.2969613113619293, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 494.074, + "cuda_time_us": 34.144, + "pct_cuda_time": 0.0606785537797004, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.144, + "pct_cuda_time": 0.0606785537797004, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 749.465, + "cuda_time_us": 50.782999999999994, + "pct_cuda_time": 0.09024833050007394, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.928, + "pct_cuda_time": 0.02297482261199528, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.607, + "pct_cuda_time": 0.0650556413488019, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.0022178665392767714, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 228.721, + "cuda_time_us": 130.078, + "pct_cuda_time": 0.23116638116670185, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 129.342, + "pct_cuda_time": 0.22985840859225662, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.877, + "cuda_time_us": 26.847, + "pct_cuda_time": 0.04771078764420152, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.847, + "pct_cuda_time": 0.04771078764420152, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 3397.544, + "cuda_time_us": 1311.022, + "pct_cuda_time": 2.3298652452369484, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 168.439, + "cuda_time_us": 783.509, + "pct_cuda_time": 1.3924025595530483, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 782.773, + "pct_cuda_time": 1.391094586978603, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.136, + "cuda_time_us": 109.598, + "pct_cuda_time": 0.19477062257344202, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.598, + "pct_cuda_time": 0.19477062257344202, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 3070.706, + "cuda_time_us": 417.915, + "pct_cuda_time": 0.7426920631104584, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0013097497110953372, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 417.178, + "pct_cuda_time": 0.741382313399363, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 3003.454, + "cuda_time_us": 1742.6329999999998, + "pct_cuda_time": 3.0968969719066495, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 155.339, + "cuda_time_us": 25.632, + "pct_cuda_time": 0.04555156661437678, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 25.632, + "pct_cuda_time": 0.04555156661437678, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2157.299, + "cuda_time_us": 385.948, + "pct_cuda_time": 0.6858823358179419, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 183.146, + "cuda_time_us": 172.285, + "pct_cuda_time": 0.30617398775584825, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 171.549, + "pct_cuda_time": 0.304866015181403, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 661.823, + "cuda_time_us": 32.896, + "pct_cuda_time": 0.05846068724042363, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.896, + "pct_cuda_time": 0.05846068724042363, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 911.811, + "cuda_time_us": 51.072, + "pct_cuda_time": 0.09076192299194175, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.896, + "pct_cuda_time": 0.022917954239193312, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.896, + "pct_cuda_time": 0.06556923384066969, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0022747349120787403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 212.033, + "cuda_time_us": 129.695, + "pct_cuda_time": 0.23048573782972828, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.959, + "pct_cuda_time": 0.22917776525528305, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.867, + "cuda_time_us": 26.175, + "pct_cuda_time": 0.04651655181536018, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.175, + "pct_cuda_time": 0.04651655181536018, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 498.106, + "cuda_time_us": 1304.878, + "pct_cuda_time": 2.3189465176589703, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 175.555, + "cuda_time_us": 782.773, + "pct_cuda_time": 1.391094586978603, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 782.037, + "pct_cuda_time": 1.3897866144041577, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 113.113, + "cuda_time_us": 109.631, + "pct_cuda_time": 0.19482926808289405, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.631, + "pct_cuda_time": 0.19482926808289405, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.854, + "cuda_time_us": 412.474, + "pct_cuda_time": 0.7330226625974737, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 411.738, + "pct_cuda_time": 0.7317146900230285, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2886.168, + "cuda_time_us": 1741.187, + "pct_cuda_time": 3.0943272323106603, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.452, + "cuda_time_us": 26.304, + "pct_cuda_time": 0.04674580244321811, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.304, + "pct_cuda_time": 0.04674580244321811, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2106.337, + "cuda_time_us": 382.29599999999994, + "pct_cuda_time": 0.6793922327719171, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.12, + "cuda_time_us": 167.326, + "pct_cuda_time": 0.2973611671081932, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.59, + "pct_cuda_time": 0.29605319453374795, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 739.163, + "cuda_time_us": 34.175, + "pct_cuda_time": 0.060733645015852294, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.175, + "pct_cuda_time": 0.060733645015852294, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 801.261, + "cuda_time_us": 51.357, + "pct_cuda_time": 0.09126840693720926, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.959, + "pct_cuda_time": 0.023029913848147184, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.927, + "pct_cuda_time": 0.06562432507682159, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.471, + "pct_cuda_time": 0.00261416801224049, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 239.015, + "cuda_time_us": 129.438, + "pct_cuda_time": 0.23002901371066248, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.702, + "pct_cuda_time": 0.22872104113621722, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 92.443, + "cuda_time_us": 26.655, + "pct_cuda_time": 0.0473695774073897, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.655, + "pct_cuda_time": 0.0473695774073897, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 518.912, + "cuda_time_us": 1305.932, + "pct_cuda_time": 2.3208196196881357, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 195.858, + "cuda_time_us": 783.317, + "pct_cuda_time": 1.3920613493162364, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 782.581, + "pct_cuda_time": 1.3907533767417912, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 105.171, + "cuda_time_us": 109.63, + "pct_cuda_time": 0.19482749094624396, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.63, + "pct_cuda_time": 0.19482749094624396, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 163.055, + "cuda_time_us": 412.985, + "pct_cuda_time": 0.7339307794256552, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0013061954377952142, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 412.25, + "pct_cuda_time": 0.7326245839878599, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2527.754, + "cuda_time_us": 1740.327, + "pct_cuda_time": 3.0927988947916076, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 110.457, + "cuda_time_us": 25.567, + "pct_cuda_time": 0.04543605273212278, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 25.567, + "pct_cuda_time": 0.04543605273212278, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1787.463, + "cuda_time_us": 382.682, + "pct_cuda_time": 0.6800782075188411, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.355, + "cuda_time_us": 167.07, + "pct_cuda_time": 0.29690622012577744, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.334, + "pct_cuda_time": 0.2955982475513322, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 504.495, + "cuda_time_us": 34.4, + "pct_cuda_time": 0.06113350076211615, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.4, + "pct_cuda_time": 0.06113350076211615, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 783.367, + "cuda_time_us": 51.582, + "pct_cuda_time": 0.09166826268347311, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 13.119, + "pct_cuda_time": 0.023314255712157027, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.959, + "pct_cuda_time": 0.06568119344962357, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.0026728135216925195, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 199.974, + "cuda_time_us": 129.63, + "pct_cuda_time": 0.23037022394747428, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.894, + "pct_cuda_time": 0.22906225137302905, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.213, + "cuda_time_us": 27.136, + "pct_cuda_time": 0.04822438013606929, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 27.136, + "pct_cuda_time": 0.04822438013606929, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 466.503, + "cuda_time_us": 1304.942, + "pct_cuda_time": 2.3190602544045746, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.463, + "cuda_time_us": 781.525, + "pct_cuda_time": 1.388876720439326, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 780.789, + "pct_cuda_time": 1.3875687478648808, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.077, + "cuda_time_us": 109.886, + "pct_cuda_time": 0.19528243792865974, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.886, + "pct_cuda_time": 0.19528243792865974, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.523, + "cuda_time_us": 413.531, + "pct_cuda_time": 0.7349010960365887, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 412.795, + "pct_cuda_time": 0.7335931234621434, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2440.3, + "cuda_time_us": 1735.048, + "pct_cuda_time": 3.083417390415933, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.073, + "cuda_time_us": 26.591, + "pct_cuda_time": 0.04725584066178577, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.591, + "pct_cuda_time": 0.04725584066178577, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1725.693, + "cuda_time_us": 380.188, + "pct_cuda_time": 0.6756460287135876, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 147.988, + "cuda_time_us": 166.59, + "pct_cuda_time": 0.29605319453374795, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0013097497110953372, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 165.853, + "pct_cuda_time": 0.2947434448226526, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 487.704, + "cuda_time_us": 33.888, + "pct_cuda_time": 0.06022360679728465, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.888, + "pct_cuda_time": 0.06022360679728465, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 729.164, + "cuda_time_us": 50.495, + "pct_cuda_time": 0.08973651514485624, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.607, + "pct_cuda_time": 0.02240436174732553, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.608, + "pct_cuda_time": 0.06505741848545198, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0022747349120787403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 202.714, + "cuda_time_us": 129.215, + "pct_cuda_time": 0.22963271223769877, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.479, + "pct_cuda_time": 0.22832473966325353, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.042, + "cuda_time_us": 26.079, + "pct_cuda_time": 0.04634594669695427, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.079, + "pct_cuda_time": 0.04634594669695427, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 485.099, + "cuda_time_us": 1302.19, + "pct_cuda_time": 2.3141695743436053, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 174.66, + "cuda_time_us": 780.949, + "pct_cuda_time": 1.3878530897288905, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 780.213, + "pct_cuda_time": 1.3865451171544454, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.652, + "cuda_time_us": 109.759, + "pct_cuda_time": 0.1950567415741019, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.759, + "pct_cuda_time": 0.1950567415741019, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 153.009, + "cuda_time_us": 411.48199999999997, + "pct_cuda_time": 0.7312597430406126, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 410.746, + "pct_cuda_time": 0.7299517704661673, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2525.07, + "cuda_time_us": 1735.785, + "pct_cuda_time": 3.084727140127028, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.997, + "cuda_time_us": 25.919, + "pct_cuda_time": 0.04606160483294443, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 25.919, + "pct_cuda_time": 0.04606160483294443, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1775.686, + "cuda_time_us": 380.443, + "pct_cuda_time": 0.6760991985593532, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 165.588, + "cuda_time_us": 167.421, + "pct_cuda_time": 0.29752999508994904, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.685, + "pct_cuda_time": 0.2962220225155038, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 498.171, + "cuda_time_us": 33.696, + "pct_cuda_time": 0.05988239656047283, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.696, + "pct_cuda_time": 0.05988239656047283, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 742.376, + "cuda_time_us": 50.624, + "pct_cuda_time": 0.08996576577271419, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.96, + "pct_cuda_time": 0.023031690984797244, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.256, + "pct_cuda_time": 0.06443186638463032, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.408, + "pct_cuda_time": 0.002502208403286614, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 222.113, + "cuda_time_us": 128.702, + "pct_cuda_time": 0.22872104113621722, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 127.966, + "pct_cuda_time": 0.22741306856177193, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.919, + "cuda_time_us": 27.136, + "pct_cuda_time": 0.04822438013606929, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 27.136, + "pct_cuda_time": 0.04822438013606929, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 530.729, + "cuda_time_us": 1302.287, + "pct_cuda_time": 2.3143419565986614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.501, + "cuda_time_us": 781.462, + "pct_cuda_time": 1.3887647608303724, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 780.726, + "pct_cuda_time": 1.387456788255927, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.972, + "cuda_time_us": 109.95, + "pct_cuda_time": 0.19539617467426365, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.95, + "pct_cuda_time": 0.19539617467426365, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 219.823, + "cuda_time_us": 410.875, + "pct_cuda_time": 0.7301810210940253, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 410.139, + "pct_cuda_time": 0.72887304851958, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2431.968, + "cuda_time_us": 1739.848, + "pct_cuda_time": 3.091947646336228, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.842, + "cuda_time_us": 26.816, + "pct_cuda_time": 0.04765569640804961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.816, + "pct_cuda_time": 0.04765569640804961, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1732.02, + "cuda_time_us": 380.09000000000003, + "pct_cuda_time": 0.6754718693218816, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.3, + "cuda_time_us": 166.46099999999998, + "pct_cuda_time": 0.29582394390588995, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 165.725, + "pct_cuda_time": 0.2945159713314447, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 477.312, + "cuda_time_us": 33.632, + "pct_cuda_time": 0.059768659814868896, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.632, + "pct_cuda_time": 0.059768659814868896, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 773.27, + "cuda_time_us": 51.199000000000005, + "pct_cuda_time": 0.09098761934649956, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.896, + "pct_cuda_time": 0.022917954239193312, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.831, + "pct_cuda_time": 0.0654537199584157, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.472, + "pct_cuda_time": 0.002615945148890551, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 192.641, + "cuda_time_us": 128.798, + "pct_cuda_time": 0.2288916462546231, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.062, + "pct_cuda_time": 0.22758367368017787, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.757, + "cuda_time_us": 26.304, + "pct_cuda_time": 0.04674580244321811, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.304, + "pct_cuda_time": 0.04674580244321811, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 479.101, + "cuda_time_us": 1306.638, + "pct_cuda_time": 2.3220742781630785, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 161.172, + "cuda_time_us": 782.837, + "pct_cuda_time": 1.3912083237242068, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 782.101, + "pct_cuda_time": 1.3899003511497616, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 121.128, + "cuda_time_us": 109.598, + "pct_cuda_time": 0.19477062257344202, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.598, + "pct_cuda_time": 0.19477062257344202, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.416, + "cuda_time_us": 414.203, + "pct_cuda_time": 0.73609533186543, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 413.467, + "pct_cuda_time": 0.7347873592909847, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2410.953, + "cuda_time_us": 1736.453, + "pct_cuda_time": 3.085914267409269, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.951, + "cuda_time_us": 27.583, + "pct_cuda_time": 0.04901876021864679, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 27.583, + "pct_cuda_time": 0.04901876021864679, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1731.876, + "cuda_time_us": 380.986, + "pct_cuda_time": 0.6770641837603367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 140.846, + "cuda_time_us": 167.422, + "pct_cuda_time": 0.2975317722265991, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.654, + "pct_cuda_time": 0.29616693127935184, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 511.106, + "cuda_time_us": 34.112, + "pct_cuda_time": 0.060621685406898426, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.112, + "pct_cuda_time": 0.060621685406898426, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 743.478, + "cuda_time_us": 51.261, + "pct_cuda_time": 0.09109780181880336, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.863, + "pct_cuda_time": 0.02285930872974128, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.959, + "pct_cuda_time": 0.06568119344962357, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.439, + "pct_cuda_time": 0.0025572996394385215, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 194.644, + "cuda_time_us": 128.191, + "pct_cuda_time": 0.22781292430803576, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 127.423, + "pct_cuda_time": 0.22644808336078856, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.578, + "cuda_time_us": 26.496, + "pct_cuda_time": 0.047087012680029926, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.496, + "pct_cuda_time": 0.047087012680029926, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 462.031, + "cuda_time_us": 1301.388, + "pct_cuda_time": 2.312744310750256, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.376, + "cuda_time_us": 779.636, + "pct_cuda_time": 1.38551970930736, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0013061954377952142, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 778.901, + "pct_cuda_time": 1.3842135138695648, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.631, + "cuda_time_us": 109.918, + "pct_cuda_time": 0.19533930630146173, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.918, + "pct_cuda_time": 0.19533930630146173, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 153.56, + "cuda_time_us": 411.834, + "pct_cuda_time": 0.7318852951414343, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 411.098, + "pct_cuda_time": 0.730577322566989, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2535.303, + "cuda_time_us": 1735.914, + "pct_cuda_time": 3.0849563907548863, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.242, + "cuda_time_us": 26.399, + "pct_cuda_time": 0.04691463042497396, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.399, + "pct_cuda_time": 0.04691463042497396, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1834.955, + "cuda_time_us": 380.98799999999994, + "pct_cuda_time": 0.6770677380336366, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.95, + "cuda_time_us": 167.03799999999998, + "pct_cuda_time": 0.2968493517529755, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.302, + "pct_cuda_time": 0.2955413791785302, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 510.446, + "cuda_time_us": 34.175, + "pct_cuda_time": 0.060733645015852294, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.175, + "pct_cuda_time": 0.060733645015852294, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 748.043, + "cuda_time_us": 50.497, + "pct_cuda_time": 0.08974006941815638, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.801, + "pct_cuda_time": 0.022749126257437465, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.416, + "pct_cuda_time": 0.06471620824864016, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0022747349120787403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 199.667, + "cuda_time_us": 129.278, + "pct_cuda_time": 0.22974467184665262, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.542, + "pct_cuda_time": 0.22843669927220736, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.89, + "cuda_time_us": 26.144, + "pct_cuda_time": 0.04646146057920827, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.144, + "pct_cuda_time": 0.04646146057920827, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 474.207, + "cuda_time_us": 1302.383, + "pct_cuda_time": 2.314512561717067, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.963, + "cuda_time_us": 780.437, + "pct_cuda_time": 1.3869431957640592, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 779.701, + "pct_cuda_time": 1.385635223189614, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 113.591, + "cuda_time_us": 109.599, + "pct_cuda_time": 0.19477239971009208, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.599, + "pct_cuda_time": 0.19477239971009208, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 152.083, + "cuda_time_us": 412.347, + "pct_cuda_time": 0.7327969662429159, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 411.579, + "pct_cuda_time": 0.7314321252956686, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2677.625, + "cuda_time_us": 1739.049, + "pct_cuda_time": 3.090527714152829, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.059, + "cuda_time_us": 25.984, + "pct_cuda_time": 0.04617711871519843, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 25.984, + "pct_cuda_time": 0.04617711871519843, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2004.791, + "cuda_time_us": 380.571, + "pct_cuda_time": 0.6763266720505612, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.04, + "cuda_time_us": 166.365, + "pct_cuda_time": 0.2956533387874841, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0013061954377952142, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 165.63, + "pct_cuda_time": 0.29434714334968887, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 500.398, + "cuda_time_us": 34.432, + "pct_cuda_time": 0.06119036913491811, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.432, + "pct_cuda_time": 0.06119036913491811, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1003.732, + "cuda_time_us": 50.816, + "pct_cuda_time": 0.09030697600952599, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.544, + "pct_cuda_time": 0.022292402138371657, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.992, + "pct_cuda_time": 0.06573983895907559, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0022747349120787403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 208.884, + "cuda_time_us": 128.958, + "pct_cuda_time": 0.22917598811863296, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.222, + "pct_cuda_time": 0.22786801554418773, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.761, + "cuda_time_us": 26.527, + "pct_cuda_time": 0.047142103916181836, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.527, + "pct_cuda_time": 0.047142103916181836, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 446.364, + "cuda_time_us": 1305.967, + "pct_cuda_time": 2.320881819470888, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.15, + "cuda_time_us": 783.157, + "pct_cuda_time": 1.3917770074522267, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 782.421, + "pct_cuda_time": 1.3904690348777813, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.667, + "cuda_time_us": 109.791, + "pct_cuda_time": 0.19511360994690388, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.791, + "pct_cuda_time": 0.19511360994690388, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.398, + "cuda_time_us": 413.019, + "pct_cuda_time": 0.7339912020717573, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0013097497110953372, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 412.282, + "pct_cuda_time": 0.7326814523606618, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2504.754, + "cuda_time_us": 1733.799, + "pct_cuda_time": 3.0811977467400062, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 64.924, + "cuda_time_us": 26.591, + "pct_cuda_time": 0.04725584066178577, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.591, + "pct_cuda_time": 0.04725584066178577, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1788.34, + "cuda_time_us": 380.12100000000004, + "pct_cuda_time": 0.6755269605580335, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.492, + "cuda_time_us": 166.55700000000002, + "pct_cuda_time": 0.29599454902429595, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0013061954377952142, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 165.822, + "pct_cuda_time": 0.2946883535865007, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 495.327, + "cuda_time_us": 33.888, + "pct_cuda_time": 0.06022360679728465, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.888, + "pct_cuda_time": 0.06022360679728465, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 786.863, + "cuda_time_us": 50.846999999999994, + "pct_cuda_time": 0.09036206724567789, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.736, + "pct_cuda_time": 0.02263361237518347, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.608, + "pct_cuda_time": 0.06505741848545198, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.503, + "pct_cuda_time": 0.0026710363850424583, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 213.44, + "cuda_time_us": 128.829, + "pct_cuda_time": 0.22894673749077504, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0013061954377952142, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.094, + "pct_cuda_time": 0.22764054205297982, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 99.086, + "cuda_time_us": 26.368, + "pct_cuda_time": 0.04685953918882205, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.368, + "pct_cuda_time": 0.04685953918882205, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 486.4, + "cuda_time_us": 1300.719, + "pct_cuda_time": 2.311555406331365, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 171.577, + "cuda_time_us": 779.413, + "pct_cuda_time": 1.3851234078343961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 778.677, + "pct_cuda_time": 1.383815435259951, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.161, + "cuda_time_us": 109.471, + "pct_cuda_time": 0.19454492621888422, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.471, + "pct_cuda_time": 0.19454492621888422, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 162.711, + "cuda_time_us": 411.835, + "pct_cuda_time": 0.7318870722780844, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 411.067, + "pct_cuda_time": 0.7305222313308372, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2435.228, + "cuda_time_us": 1737.925, + "pct_cuda_time": 3.0885302125581595, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.947, + "cuda_time_us": 26.271, + "pct_cuda_time": 0.046687156933766086, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.271, + "pct_cuda_time": 0.046687156933766086, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1753.059, + "cuda_time_us": 381.01700000000005, + "pct_cuda_time": 0.6771192749964887, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 147.172, + "cuda_time_us": 167.9, + "pct_cuda_time": 0.2983812435453285, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0013061954377952142, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 167.165, + "pct_cuda_time": 0.29707504810753327, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 522.962, + "cuda_time_us": 33.632, + "pct_cuda_time": 0.059768659814868896, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.632, + "pct_cuda_time": 0.059768659814868896, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 739.119, + "cuda_time_us": 50.943000000000005, + "pct_cuda_time": 0.09053267236408381, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.575, + "pct_cuda_time": 0.02234749337452356, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.896, + "pct_cuda_time": 0.06556923384066969, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.472, + "pct_cuda_time": 0.002615945148890551, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 191.473, + "cuda_time_us": 128.542, + "pct_cuda_time": 0.22843669927220736, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 127.806, + "pct_cuda_time": 0.22712872669776207, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.607, + "cuda_time_us": 26.624, + "pct_cuda_time": 0.04731448617123779, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.624, + "pct_cuda_time": 0.04731448617123779, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 453.784, + "cuda_time_us": 1304.013, + "pct_cuda_time": 2.317409294456667, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.659, + "cuda_time_us": 782.549, + "pct_cuda_time": 1.3906965083689893, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 781.813, + "pct_cuda_time": 1.389388535794544, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.05, + "cuda_time_us": 109.823, + "pct_cuda_time": 0.19517047831970585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.823, + "pct_cuda_time": 0.19517047831970585, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.972, + "cuda_time_us": 411.641, + "pct_cuda_time": 0.7315423077679726, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.767, + "pct_cuda_time": 0.0013630638105971826, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 410.874, + "pct_cuda_time": 0.7301792439573753, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2413.609, + "cuda_time_us": 1736.36, + "pct_cuda_time": 3.085748993700814, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.126, + "cuda_time_us": 26.399, + "pct_cuda_time": 0.04691463042497396, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.399, + "pct_cuda_time": 0.04691463042497396, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1711.685, + "cuda_time_us": 381.786, + "pct_cuda_time": 0.6784858930803859, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.918, + "cuda_time_us": 166.846, + "pct_cuda_time": 0.29650814151616367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.078, + "pct_cuda_time": 0.2951433005689164, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 506.254, + "cuda_time_us": 34.207, + "pct_cuda_time": 0.06079051338865427, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.207, + "pct_cuda_time": 0.06079051338865427, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 717.196, + "cuda_time_us": 51.263, + "pct_cuda_time": 0.09110135609210347, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 13.12, + "pct_cuda_time": 0.023316032848807088, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.895, + "pct_cuda_time": 0.06556745670401963, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.0022178665392767714, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 188.643, + "cuda_time_us": 129.47, + "pct_cuda_time": 0.23008588208346445, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.734, + "pct_cuda_time": 0.2287779095090192, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.488, + "cuda_time_us": 26.304, + "pct_cuda_time": 0.04674580244321811, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.304, + "pct_cuda_time": 0.04674580244321811, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 453.877, + "cuda_time_us": 1301.8709999999999, + "pct_cuda_time": 2.3136026677522357, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 154.406, + "cuda_time_us": 780.406, + "pct_cuda_time": 1.3868881045279071, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.769, + "pct_cuda_time": 0.0013666180838973058, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 779.637, + "pct_cuda_time": 1.38552148644401, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.911, + "cuda_time_us": 109.79, + "pct_cuda_time": 0.19511183281025385, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.79, + "pct_cuda_time": 0.19511183281025385, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.109, + "cuda_time_us": 411.675, + "pct_cuda_time": 0.7316027304140745, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 410.939, + "pct_cuda_time": 0.7302947578396293, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2431.241, + "cuda_time_us": 1738.282, + "pct_cuda_time": 3.0891646503422314, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.585, + "cuda_time_us": 26.912, + "pct_cuda_time": 0.04782630152645551, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.912, + "pct_cuda_time": 0.04782630152645551, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1742.482, + "cuda_time_us": 380.924, + "pct_cuda_time": 0.6769540012880328, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 149.845, + "cuda_time_us": 167.13400000000001, + "pct_cuda_time": 0.2970199568713814, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.366, + "pct_cuda_time": 0.2956551159241342, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 468.053, + "cuda_time_us": 34.655, + "pct_cuda_time": 0.06158667060788183, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.655, + "pct_cuda_time": 0.06158667060788183, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 774.958, + "cuda_time_us": 50.367999999999995, + "pct_cuda_time": 0.08951081879029842, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.736, + "pct_cuda_time": 0.02263361237518347, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.32, + "pct_cuda_time": 0.06454560313023425, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.0023316032848807087, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 207.638, + "cuda_time_us": 128.767, + "pct_cuda_time": 0.22883655501847122, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0013097497110953372, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.03, + "pct_cuda_time": 0.2275268053073759, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.658, + "cuda_time_us": 26.08, + "pct_cuda_time": 0.04634772383360433, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.08, + "pct_cuda_time": 0.04634772383360433, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 473.757, + "cuda_time_us": 1304.366, + "pct_cuda_time": 2.318036623694139, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 175.162, + "cuda_time_us": 781.301, + "pct_cuda_time": 1.3884786418297124, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 780.565, + "pct_cuda_time": 1.3871706692552672, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.531, + "cuda_time_us": 110.047, + "pct_cuda_time": 0.19556855692931963, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 110.047, + "pct_cuda_time": 0.19556855692931963, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 147.816, + "cuda_time_us": 413.018, + "pct_cuda_time": 0.7339894249351071, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 412.25, + "pct_cuda_time": 0.7326245839878599, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2487.437, + "cuda_time_us": 1737.1260000000002, + "pct_cuda_time": 3.087110280374761, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.313, + "cuda_time_us": 26.368, + "pct_cuda_time": 0.04685953918882205, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.368, + "pct_cuda_time": 0.04685953918882205, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1791.537, + "cuda_time_us": 380.98400000000004, + "pct_cuda_time": 0.6770606294870366, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 166.528, + "cuda_time_us": 167.90099999999998, + "pct_cuda_time": 0.29838302068197853, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 167.165, + "pct_cuda_time": 0.29707504810753327, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 524.056, + "cuda_time_us": 33.919, + "pct_cuda_time": 0.060278698033436544, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.919, + "pct_cuda_time": 0.060278698033436544, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 767.899, + "cuda_time_us": 50.526, + "pct_cuda_time": 0.08979160638100815, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.64, + "pct_cuda_time": 0.02246300725677756, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.383, + "pct_cuda_time": 0.06465756273918813, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.503, + "pct_cuda_time": 0.0026710363850424583, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 193.914, + "cuda_time_us": 128.638, + "pct_cuda_time": 0.22860730439061328, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 127.87, + "pct_cuda_time": 0.22724246344336604, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.082, + "cuda_time_us": 26.816, + "pct_cuda_time": 0.04765569640804961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.816, + "pct_cuda_time": 0.04765569640804961, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 483.276, + "cuda_time_us": 1302.958, + "pct_cuda_time": 2.315534415290853, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.665, + "cuda_time_us": 781.109, + "pct_cuda_time": 1.3881374315929007, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 780.373, + "pct_cuda_time": 1.3868294590184553, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.054, + "cuda_time_us": 109.759, + "pct_cuda_time": 0.1950567415741019, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.759, + "pct_cuda_time": 0.1950567415741019, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 164.643, + "cuda_time_us": 412.09, + "pct_cuda_time": 0.7323402421238501, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 411.354, + "pct_cuda_time": 0.7310322695494048, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2475.945, + "cuda_time_us": 1735.561, + "pct_cuda_time": 3.084329061517414, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.04, + "cuda_time_us": 25.92, + "pct_cuda_time": 0.04606338196959449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 25.92, + "pct_cuda_time": 0.04606338196959449, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1748.299, + "cuda_time_us": 379.995, + "pct_cuda_time": 0.6753030413401256, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 143.278, + "cuda_time_us": 166.749, + "pct_cuda_time": 0.2963357592611077, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.013, + "pct_cuda_time": 0.2950277866866624, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 507.384, + "cuda_time_us": 33.695, + "pct_cuda_time": 0.05988061942382277, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.695, + "pct_cuda_time": 0.05988061942382277, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 748.294, + "cuda_time_us": 50.592, + "pct_cuda_time": 0.0899088973999122, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.704, + "pct_cuda_time": 0.022576744002381497, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.416, + "pct_cuda_time": 0.06471620824864016, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.472, + "pct_cuda_time": 0.002615945148890551, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 209.94, + "cuda_time_us": 128.959, + "pct_cuda_time": 0.22917776525528305, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.191, + "pct_cuda_time": 0.22781292430803576, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 93.65, + "cuda_time_us": 26.591, + "pct_cuda_time": 0.04725584066178577, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.591, + "pct_cuda_time": 0.04725584066178577, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 496.974, + "cuda_time_us": 1303.0549999999998, + "pct_cuda_time": 2.315706797545908, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 183.995, + "cuda_time_us": 782.294, + "pct_cuda_time": 1.3902433385232233, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 781.558, + "pct_cuda_time": 1.3889353659487782, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.644, + "cuda_time_us": 110.079, + "pct_cuda_time": 0.19562542530212162, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 110.079, + "pct_cuda_time": 0.19562542530212162, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 158.484, + "cuda_time_us": 410.682, + "pct_cuda_time": 0.7298380337205636, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 409.946, + "pct_cuda_time": 0.7285300611461183, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2802.835, + "cuda_time_us": 1736.33, + "pct_cuda_time": 3.085695679601312, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.349, + "cuda_time_us": 25.568, + "pct_cuda_time": 0.04543782986877284, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 25.568, + "pct_cuda_time": 0.04543782986877284, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2113.857, + "cuda_time_us": 380.188, + "pct_cuda_time": 0.6756460287135876, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 173.501, + "cuda_time_us": 166.43, + "pct_cuda_time": 0.2957688526697381, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 165.662, + "pct_cuda_time": 0.2944040117224908, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 516.143, + "cuda_time_us": 34.144, + "pct_cuda_time": 0.0606785537797004, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.144, + "pct_cuda_time": 0.0606785537797004, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1045.023, + "cuda_time_us": 50.911, + "pct_cuda_time": 0.09047580399128184, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.768, + "pct_cuda_time": 0.022690480747985436, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.863, + "pct_cuda_time": 0.06551058833121766, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0022747349120787403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 232.334, + "cuda_time_us": 128.703, + "pct_cuda_time": 0.2287228182728673, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 127.967, + "pct_cuda_time": 0.22741484569842202, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.073, + "cuda_time_us": 26.815, + "pct_cuda_time": 0.047653919271399545, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.815, + "pct_cuda_time": 0.047653919271399545, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 463.591, + "cuda_time_us": 1303.759, + "pct_cuda_time": 2.316957901747552, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 162.31, + "cuda_time_us": 781.654, + "pct_cuda_time": 1.3891059710671843, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 780.918, + "pct_cuda_time": 1.3877979984927389, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.568, + "cuda_time_us": 109.31, + "pct_cuda_time": 0.1942588072182243, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.31, + "pct_cuda_time": 0.1942588072182243, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.012, + "cuda_time_us": 412.79499999999996, + "pct_cuda_time": 0.7335931234621433, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 412.027, + "pct_cuda_time": 0.7322282825148961, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2366.646, + "cuda_time_us": 1735.8519999999999, + "pct_cuda_time": 3.084846208282582, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.236, + "cuda_time_us": 26.239, + "pct_cuda_time": 0.046630288560964114, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.239, + "pct_cuda_time": 0.046630288560964114, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1696.07, + "cuda_time_us": 379.70799999999997, + "pct_cuda_time": 0.674793003121558, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 149.163, + "cuda_time_us": 166.846, + "pct_cuda_time": 0.29650814151616367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.11, + "pct_cuda_time": 0.2952001689417184, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 486.146, + "cuda_time_us": 33.887, + "pct_cuda_time": 0.060221829660634586, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.887, + "pct_cuda_time": 0.060221829660634586, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 726.62, + "cuda_time_us": 50.239999999999995, + "pct_cuda_time": 0.08928334529909056, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.672, + "pct_cuda_time": 0.02251987562957953, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.256, + "pct_cuda_time": 0.06443186638463032, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.0023316032848807087, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 196.618, + "cuda_time_us": 128.73499999999999, + "pct_cuda_time": 0.22877968664566922, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 127.999, + "pct_cuda_time": 0.22747171407122396, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.232, + "cuda_time_us": 26.431, + "pct_cuda_time": 0.04697149879777593, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.431, + "pct_cuda_time": 0.04697149879777593, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 450.273, + "cuda_time_us": 1303.474, + "pct_cuda_time": 2.316451417802284, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.232, + "cuda_time_us": 781.366, + "pct_cuda_time": 1.3885941557119663, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 780.63, + "pct_cuda_time": 1.3872861831375212, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.993, + "cuda_time_us": 109.567, + "pct_cuda_time": 0.19471553133729008, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.567, + "pct_cuda_time": 0.19471553133729008, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.595, + "cuda_time_us": 412.541, + "pct_cuda_time": 0.7331417307530278, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0013097497110953372, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 411.804, + "pct_cuda_time": 0.7318319810419324, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2527.597, + "cuda_time_us": 1738.694, + "pct_cuda_time": 3.0898968306420573, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.789, + "cuda_time_us": 25.855, + "pct_cuda_time": 0.04594786808734049, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 25.855, + "pct_cuda_time": 0.04594786808734049, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1815.261, + "cuda_time_us": 381.466, + "pct_cuda_time": 0.6779172093523662, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 157.713, + "cuda_time_us": 167.518, + "pct_cuda_time": 0.29770237734500504, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.782, + "pct_cuda_time": 0.2963944047705598, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 521.676, + "cuda_time_us": 33.407, + "pct_cuda_time": 0.059368804068605056, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.407, + "pct_cuda_time": 0.059368804068605056, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 763.133, + "cuda_time_us": 51.519, + "pct_cuda_time": 0.09155630307451923, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.8, + "pct_cuda_time": 0.022747349120787404, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 37.215, + "pct_cuda_time": 0.06613614043203932, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.0026728135216925195, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 224.882, + "cuda_time_us": 129.022, + "pct_cuda_time": 0.22928972486423688, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.286, + "pct_cuda_time": 0.22798175228979164, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.104, + "cuda_time_us": 25.151, + "pct_cuda_time": 0.04469676388569719, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 25.151, + "pct_cuda_time": 0.04469676388569719, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 482.007, + "cuda_time_us": 1306.222, + "pct_cuda_time": 2.3213349893166537, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 172.708, + "cuda_time_us": 783.67, + "pct_cuda_time": 1.392688678553708, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 782.934, + "pct_cuda_time": 1.3913807059792627, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.537, + "cuda_time_us": 109.534, + "pct_cuda_time": 0.1946568858278381, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.534, + "pct_cuda_time": 0.1946568858278381, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 156.736, + "cuda_time_us": 413.018, + "pct_cuda_time": 0.7339894249351071, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 412.282, + "pct_cuda_time": 0.7326814523606618, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2448.72, + "cuda_time_us": 1734.826, + "pct_cuda_time": 3.0830228660796193, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.077, + "cuda_time_us": 25.696, + "pct_cuda_time": 0.045665303359980716, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 25.696, + "pct_cuda_time": 0.045665303359980716, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1721.12, + "cuda_time_us": 380.31600000000003, + "pct_cuda_time": 0.6758735022047956, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.15, + "cuda_time_us": 167.55, + "pct_cuda_time": 0.297759245717807, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.782, + "pct_cuda_time": 0.2963944047705598, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 492.687, + "cuda_time_us": 33.696, + "pct_cuda_time": 0.05988239656047283, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.696, + "pct_cuda_time": 0.05988239656047283, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 739.693, + "cuda_time_us": 50.207, + "pct_cuda_time": 0.08922469978963853, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.672, + "pct_cuda_time": 0.02251987562957953, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.063, + "pct_cuda_time": 0.06408887901116844, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.472, + "pct_cuda_time": 0.002615945148890551, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 200.738, + "cuda_time_us": 128.863, + "pct_cuda_time": 0.2290071601368771, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0013097497110953372, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.126, + "pct_cuda_time": 0.2276974104257818, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 94.626, + "cuda_time_us": 27.136, + "pct_cuda_time": 0.04822438013606929, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 27.136, + "pct_cuda_time": 0.04822438013606929, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 485.926, + "cuda_time_us": 1301.6779999999999, + "pct_cuda_time": 2.3132596803787737, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.445, + "cuda_time_us": 780.821, + "pct_cuda_time": 1.387625616237683, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 780.085, + "pct_cuda_time": 1.3863176436632378, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 112.472, + "cuda_time_us": 109.567, + "pct_cuda_time": 0.19471553133729008, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.567, + "pct_cuda_time": 0.19471553133729008, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 156.316, + "cuda_time_us": 411.28999999999996, + "pct_cuda_time": 0.7309185328038007, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 410.554, + "pct_cuda_time": 0.7296105602293556, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2484.586, + "cuda_time_us": 1741.095, + "pct_cuda_time": 3.094163735738855, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.43, + "cuda_time_us": 26.272, + "pct_cuda_time": 0.04668893407041614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.272, + "pct_cuda_time": 0.04668893407041614, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1794.734, + "cuda_time_us": 380.73, + "pct_cuda_time": 0.676609236777921, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 169.351, + "cuda_time_us": 166.845, + "pct_cuda_time": 0.2965063643795136, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.077, + "pct_cuda_time": 0.29514152343226635, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 513.529, + "cuda_time_us": 34.559, + "pct_cuda_time": 0.06141606548947592, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.559, + "pct_cuda_time": 0.06141606548947592, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 758.591, + "cuda_time_us": 50.655, + "pct_cuda_time": 0.09002085700886608, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.672, + "pct_cuda_time": 0.02251987562957953, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.703, + "pct_cuda_time": 0.06522624646720782, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0022747349120787403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 206.485, + "cuda_time_us": 128.671, + "pct_cuda_time": 0.22866594990006528, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0013097497110953372, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 127.934, + "pct_cuda_time": 0.22735620018896996, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.592, + "cuda_time_us": 27.104, + "pct_cuda_time": 0.04816751176326732, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 27.104, + "pct_cuda_time": 0.04816751176326732, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 464.319, + "cuda_time_us": 1306.989, + "pct_cuda_time": 2.3226980531272505, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.488, + "cuda_time_us": 782.964, + "pct_cuda_time": 1.3914340200787647, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0013061954377952142, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 782.229, + "pct_cuda_time": 1.3901278246409696, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.309, + "cuda_time_us": 109.726, + "pct_cuda_time": 0.19499809606464988, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.726, + "pct_cuda_time": 0.19499809606464988, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.519, + "cuda_time_us": 414.299, + "pct_cuda_time": 0.7362659369838359, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 413.531, + "pct_cuda_time": 0.7349010960365887, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2429.488, + "cuda_time_us": 1734.279, + "pct_cuda_time": 3.0820507723320354, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.028, + "cuda_time_us": 26.816, + "pct_cuda_time": 0.04765569640804961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.816, + "pct_cuda_time": 0.04765569640804961, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1741.213, + "cuda_time_us": 380.442, + "pct_cuda_time": 0.6760974214227032, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.652, + "cuda_time_us": 167.07, + "pct_cuda_time": 0.29690622012577744, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.334, + "pct_cuda_time": 0.2955982475513322, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 469.489, + "cuda_time_us": 33.887, + "pct_cuda_time": 0.060221829660634586, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.887, + "pct_cuda_time": 0.060221829660634586, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 783.742, + "cuda_time_us": 50.590999999999994, + "pct_cuda_time": 0.08990712026326214, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.736, + "pct_cuda_time": 0.02263361237518347, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.543, + "pct_cuda_time": 0.06494190460319797, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.0023316032848807087, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 196.411, + "cuda_time_us": 128.89399999999998, + "pct_cuda_time": 0.229062251373029, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.158, + "pct_cuda_time": 0.22775427879858373, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.535, + "cuda_time_us": 26.144, + "pct_cuda_time": 0.04646146057920827, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.144, + "pct_cuda_time": 0.04646146057920827, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.335, + "cuda_time_us": 1300.877, + "pct_cuda_time": 2.3118361939220744, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 162.443, + "cuda_time_us": 778.997, + "pct_cuda_time": 1.3843841189879706, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 778.261, + "pct_cuda_time": 1.3830761464135253, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.699, + "cuda_time_us": 110.015, + "pct_cuda_time": 0.19551168855651765, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 110.015, + "pct_cuda_time": 0.19551168855651765, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 158.27, + "cuda_time_us": 411.865, + "pct_cuda_time": 0.7319403863775863, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0013061954377952142, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 411.13, + "pct_cuda_time": 0.730634190939791, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2403.725, + "cuda_time_us": 1738.6009999999999, + "pct_cuda_time": 3.0897315569336015, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.672, + "cuda_time_us": 25.824, + "pct_cuda_time": 0.04589277685118859, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 25.824, + "pct_cuda_time": 0.04589277685118859, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1704.497, + "cuda_time_us": 381.81899999999996, + "pct_cuda_time": 0.6785445385898379, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 142.936, + "cuda_time_us": 167.998, + "pct_cuda_time": 0.29855540293703453, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 167.262, + "pct_cuda_time": 0.29724743036258927, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 460.095, + "cuda_time_us": 34.047, + "pct_cuda_time": 0.06050617152464443, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.047, + "pct_cuda_time": 0.06050617152464443, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 753.805, + "cuda_time_us": 50.88, + "pct_cuda_time": 0.09042071275512993, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.704, + "pct_cuda_time": 0.022576744002381497, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.703, + "pct_cuda_time": 0.06522624646720782, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.473, + "pct_cuda_time": 0.002617722285540613, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 202.098, + "cuda_time_us": 128.89399999999998, + "pct_cuda_time": 0.229062251373029, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.158, + "pct_cuda_time": 0.22775427879858373, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 90.814, + "cuda_time_us": 26.784, + "pct_cuda_time": 0.047598828035247634, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.784, + "pct_cuda_time": 0.047598828035247634, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 470.218, + "cuda_time_us": 1304.174, + "pct_cuda_time": 2.317695413457327, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.921, + "cuda_time_us": 782.293, + "pct_cuda_time": 1.3902415613865733, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 781.557, + "pct_cuda_time": 1.3889335888121281, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.595, + "cuda_time_us": 109.695, + "pct_cuda_time": 0.19494300482849797, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.695, + "pct_cuda_time": 0.19494300482849797, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.654, + "cuda_time_us": 412.186, + "pct_cuda_time": 0.732510847242256, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 411.45, + "pct_cuda_time": 0.7312028746678106, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2702.258, + "cuda_time_us": 1737.959, + "pct_cuda_time": 3.088590635204262, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.128, + "cuda_time_us": 26.24, + "pct_cuda_time": 0.046632065697614175, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.24, + "pct_cuda_time": 0.046632065697614175, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2005.119, + "cuda_time_us": 380.888, + "pct_cuda_time": 0.6768900243686305, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 154.048, + "cuda_time_us": 167.38899999999998, + "pct_cuda_time": 0.29747312671714704, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.653, + "pct_cuda_time": 0.2961651541427018, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 528.646, + "cuda_time_us": 34.207, + "pct_cuda_time": 0.06079051338865427, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.207, + "pct_cuda_time": 0.06079051338865427, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 949.686, + "cuda_time_us": 50.59, + "pct_cuda_time": 0.0899053431266121, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.703, + "pct_cuda_time": 0.022574966865731436, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.447, + "pct_cuda_time": 0.06477129948479207, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.44, + "pct_cuda_time": 0.0025590767760885827, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 210.179, + "cuda_time_us": 128.702, + "pct_cuda_time": 0.22872104113621722, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 127.966, + "pct_cuda_time": 0.22741306856177193, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.845, + "cuda_time_us": 26.72, + "pct_cuda_time": 0.047485091289643705, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.72, + "pct_cuda_time": 0.047485091289643705, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.681, + "cuda_time_us": 1304.111, + "pct_cuda_time": 2.3175834538483735, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.371, + "cuda_time_us": 781.717, + "pct_cuda_time": 1.3892179306761379, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 780.981, + "pct_cuda_time": 1.3879099581016927, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.257, + "cuda_time_us": 109.791, + "pct_cuda_time": 0.19511360994690388, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.791, + "pct_cuda_time": 0.19511360994690388, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 156.288, + "cuda_time_us": 412.603, + "pct_cuda_time": 0.7332519132253317, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 411.867, + "pct_cuda_time": 0.7319439406508863, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2428.616, + "cuda_time_us": 1739.6219999999998, + "pct_cuda_time": 3.091546013453314, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.749, + "cuda_time_us": 26.079, + "pct_cuda_time": 0.04634594669695427, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.079, + "pct_cuda_time": 0.04634594669695427, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1755.635, + "cuda_time_us": 382.491, + "pct_cuda_time": 0.6797387744186792, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 149.79, + "cuda_time_us": 168.06199999999998, + "pct_cuda_time": 0.2986691396826384, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 167.326, + "pct_cuda_time": 0.2973611671081932, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 516.584, + "cuda_time_us": 33.663, + "pct_cuda_time": 0.05982375105102081, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.663, + "pct_cuda_time": 0.05982375105102081, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 754.985, + "cuda_time_us": 50.912, + "pct_cuda_time": 0.09047758112793189, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.768, + "pct_cuda_time": 0.022690480747985436, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.64, + "pct_cuda_time": 0.06511428685825393, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.0026728135216925195, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 193.582, + "cuda_time_us": 129.85399999999998, + "pct_cuda_time": 0.23076830255708805, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 129.118, + "pct_cuda_time": 0.22946032998264282, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.065, + "cuda_time_us": 26.079, + "pct_cuda_time": 0.04634594669695427, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.079, + "pct_cuda_time": 0.04634594669695427, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 462.299, + "cuda_time_us": 1304.973, + "pct_cuda_time": 2.3191153456407267, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 161.388, + "cuda_time_us": 782.741, + "pct_cuda_time": 1.391037718605801, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 782.005, + "pct_cuda_time": 1.3897297460313558, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.718, + "cuda_time_us": 109.503, + "pct_cuda_time": 0.19460179459168617, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.503, + "pct_cuda_time": 0.19460179459168617, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.296, + "cuda_time_us": 412.729, + "pct_cuda_time": 0.7334758324432393, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.767, + "pct_cuda_time": 0.0013630638105971826, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 411.962, + "pct_cuda_time": 0.7321127686326422, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2360.475, + "cuda_time_us": 1734.6000000000001, + "pct_cuda_time": 3.0826212331967056, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.367, + "cuda_time_us": 26.207, + "pct_cuda_time": 0.04657342018816215, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.207, + "pct_cuda_time": 0.04657342018816215, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1681.601, + "cuda_time_us": 380.221, + "pct_cuda_time": 0.6757046742230396, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 153.016, + "cuda_time_us": 166.879, + "pct_cuda_time": 0.2965667870256157, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0013097497110953372, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.142, + "pct_cuda_time": 0.29525703731452035, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 502.198, + "cuda_time_us": 33.76, + "pct_cuda_time": 0.059996133306076775, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.76, + "pct_cuda_time": 0.059996133306076775, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 711.404, + "cuda_time_us": 50.206999999999994, + "pct_cuda_time": 0.08922469978963851, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.703, + "pct_cuda_time": 0.022574966865731436, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.224, + "pct_cuda_time": 0.06437499801182835, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0022747349120787403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 186.759, + "cuda_time_us": 129.375, + "pct_cuda_time": 0.22991705410170862, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.639, + "pct_cuda_time": 0.22860908152726334, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.998, + "cuda_time_us": 26.463, + "pct_cuda_time": 0.047028367170577894, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.463, + "pct_cuda_time": 0.047028367170577894, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 472.439, + "cuda_time_us": 1301.709, + "pct_cuda_time": 2.313314771614926, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.147, + "cuda_time_us": 780.148, + "pct_cuda_time": 1.3864296032721914, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0013061954377952142, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 779.413, + "pct_cuda_time": 1.3851234078343961, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 91.763, + "cuda_time_us": 109.695, + "pct_cuda_time": 0.19494300482849797, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.695, + "pct_cuda_time": 0.19494300482849797, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 176.843, + "cuda_time_us": 411.866, + "pct_cuda_time": 0.7319421635142362, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 411.13, + "pct_cuda_time": 0.730634190939791, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2569.828, + "cuda_time_us": 1742.0579999999998, + "pct_cuda_time": 3.095875118332864, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.638, + "cuda_time_us": 25.76, + "pct_cuda_time": 0.045779040105584645, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 25.76, + "pct_cuda_time": 0.045779040105584645, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1872.215, + "cuda_time_us": 381.11699999999996, + "pct_cuda_time": 0.6772969886614947, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.007, + "cuda_time_us": 166.142, + "pct_cuda_time": 0.29525703731452035, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 165.406, + "pct_cuda_time": 0.2939490647400751, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 521.055, + "cuda_time_us": 34.368, + "pct_cuda_time": 0.06107663238931418, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.368, + "pct_cuda_time": 0.06107663238931418, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 861.87, + "cuda_time_us": 51.424, + "pct_cuda_time": 0.09138747509276339, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 13.247, + "pct_cuda_time": 0.0235417292033649, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.608, + "pct_cuda_time": 0.06505741848545198, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.569, + "pct_cuda_time": 0.0027883274039465183, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 200.055, + "cuda_time_us": 129.183, + "pct_cuda_time": 0.2295758438648968, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.447, + "pct_cuda_time": 0.2282678712904515, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.13, + "cuda_time_us": 27.135, + "pct_cuda_time": 0.04822260299941923, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 27.135, + "pct_cuda_time": 0.04822260299941923, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 474.521, + "cuda_time_us": 1308.0459999999998, + "pct_cuda_time": 2.3245764865663654, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 168.657, + "cuda_time_us": 783.5409999999999, + "pct_cuda_time": 1.39245942792585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 782.805, + "pct_cuda_time": 1.3911514553514048, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.594, + "cuda_time_us": 109.823, + "pct_cuda_time": 0.19517047831970585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.823, + "pct_cuda_time": 0.19517047831970585, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 153.334, + "cuda_time_us": 414.68199999999996, + "pct_cuda_time": 0.7369465803208094, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 413.914, + "pct_cuda_time": 0.7355817393735623, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2418.348, + "cuda_time_us": 1734.4740000000002, + "pct_cuda_time": 3.082397313978798, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.13, + "cuda_time_us": 26.144, + "pct_cuda_time": 0.04646146057920827, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.144, + "pct_cuda_time": 0.04646146057920827, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1735.792, + "cuda_time_us": 380.731, + "pct_cuda_time": 0.676611013914571, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.604, + "cuda_time_us": 167.45399999999998, + "pct_cuda_time": 0.29758864059940104, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.718, + "pct_cuda_time": 0.2962806680249558, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 508.154, + "cuda_time_us": 33.344, + "pct_cuda_time": 0.05925684445965119, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.344, + "pct_cuda_time": 0.05925684445965119, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 732.914, + "cuda_time_us": 50.623000000000005, + "pct_cuda_time": 0.08996398863606413, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.768, + "pct_cuda_time": 0.022690480747985436, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.383, + "pct_cuda_time": 0.06465756273918813, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.472, + "pct_cuda_time": 0.002615945148890551, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 190.787, + "cuda_time_us": 129.31, + "pct_cuda_time": 0.22980154021945465, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0013648409472472442, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 128.542, + "pct_cuda_time": 0.22843669927220736, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 94.393, + "cuda_time_us": 26.112, + "pct_cuda_time": 0.046404592206406296, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.112, + "pct_cuda_time": 0.046404592206406296, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 452.942, + "cuda_time_us": 1301.487, + "pct_cuda_time": 2.312920247278612, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 156.587, + "cuda_time_us": 779.573, + "pct_cuda_time": 1.385407749698406, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 778.837, + "pct_cuda_time": 1.3840997771239607, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.349, + "cuda_time_us": 109.919, + "pct_cuda_time": 0.19534108343811177, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.919, + "pct_cuda_time": 0.19534108343811177, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.714, + "cuda_time_us": 411.995, + "pct_cuda_time": 0.7321714141420943, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0013097497110953372, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 411.258, + "pct_cuda_time": 0.7308616644309989, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2479.5, + "cuda_time_us": 1737.2240000000002, + "pct_cuda_time": 3.087284439766467, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.657, + "cuda_time_us": 25.792, + "pct_cuda_time": 0.045835908478386624, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 25.792, + "pct_cuda_time": 0.045835908478386624, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1734.516, + "cuda_time_us": 381.435, + "pct_cuda_time": 0.6778621181162143, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.148, + "cuda_time_us": 166.815, + "pct_cuda_time": 0.29645305028001173, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 166.079, + "pct_cuda_time": 0.2951450777055665, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 484.754, + "cuda_time_us": 33.791, + "pct_cuda_time": 0.06005122454222868, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.791, + "pct_cuda_time": 0.06005122454222868, + "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 743.297, + "cuda_time_us": 50.847, + "pct_cuda_time": 0.0903620672456779, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 12.96, + "pct_cuda_time": 0.023031690984797244, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 36.607, + "pct_cuda_time": 0.0650556413488019, + "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0022747349120787403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 219.114, + "cuda_time_us": 129.982, + "pct_cuda_time": 0.23099577604829596, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 129.246, + "pct_cuda_time": 0.22968780347385068, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 116.971, + "cuda_time_us": 26.559, + "pct_cuda_time": 0.0471989722889838, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 26.559, + "pct_cuda_time": 0.0471989722889838, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 489.506, + "cuda_time_us": 1303.438, + "pct_cuda_time": 2.3163874408828824, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 174.767, + "cuda_time_us": 782.005, + "pct_cuda_time": 1.3897297460313558, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 781.269, + "pct_cuda_time": 1.3884217734569104, + "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 106.887, + "cuda_time_us": 109.567, + "pct_cuda_time": 0.19471553133729008, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 109.567, + "pct_cuda_time": 0.19471553133729008, + "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 152.72, + "cuda_time_us": 411.866, + "pct_cuda_time": 0.7319421635142362, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 411.13, + "pct_cuda_time": 0.730634190939791, + "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.909, + "cuda_time_us": 25.792, + "pct_cuda_time": 0.045835908478386624, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 25.792, + "pct_cuda_time": 0.045835908478386624, + "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 547.992, + "cuda_time_us": 396.73, + "pct_cuda_time": 0.7050434231789052, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 3.936, + "pct_cuda_time": 0.006994809854642127, + "trace": "index_select(bfloat16[2560, 4096], 0, int64[20])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[20, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 392.058, + "pct_cuda_time": 0.6967406407498178, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[20, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 4691.088, + "cuda_time_us": 138.428, + "pct_cuda_time": 0.2460054721947155, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0013079725744452755, + "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0014217093200492128, + "trace": "copy_(int32[20], int32[20], True) <- _to_copy(int32[20], 3, 0, None, None, True, None) <- to(int32[20], 3, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0014217093200492128, + "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.767, + "pct_cuda_time": 0.0013630638105971826, + "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0014217093200492128, + "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0014217093200492128, + "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 9.28, + "pct_cuda_time": 0.016491828112570866, + "trace": "copy_(float32[20, 128256], bfloat16[20, 128256], False) <- _to_copy(bfloat16[20, 128256], 6, None, None, None, False, None) <- to(bfloat16[20, 128256], 6, False, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::elementwise_kernel<128, 4, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1})", + "cpu_time_us": 0, + "cuda_time_us": 13.216, + "pct_cuda_time": 0.02348663796721299, + "trace": "div_(float32[20, 128256], bfloat16[20, 1])" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::SoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 35.391, + "pct_cuda_time": 0.06289464318232711, + "trace": "_softmax(float32[20, 128256], -1, False) <- softmax(float32[20, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::LogSoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 29.407, + "pct_cuda_time": 0.052260257468358995, + "trace": "_log_softmax(float32[20, 128256], -1, False) <- log_softmax(float32[20, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 2.08, + "pct_cuda_time": 0.0036964442321279533, + "trace": "copy_(int64[20], int32[20], False) <- _to_copy(int32[20], 4, None, None, None, False, None) <- to(int32[20], 4, False, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::index_elementwise_kernel<128, 4, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1}>(long, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1})", + "cpu_time_us": 0, + "cuda_time_us": 12.831, + "pct_cuda_time": 0.022802440356939308, + "trace": "index(float32[20, 128256], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, long, 4> >(at::native::ReduceOp, unsigned int, long, 4>)", + "cpu_time_us": 0, + "cuda_time_us": 28.0, + "pct_cuda_time": 0.049759826201722444, + "trace": "argmax(float32[20, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 2.784, + "pct_cuda_time": 0.00494754843377126, + "trace": "copy_(int64[20], int64[20], False) <- _to_copy(int64[20], 4, 0, None, None, False, None) <- to(int64[20], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + }, + "decode_1": { + "metadata": { + "num_running_seqs": 20 + }, + "summary_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cuda_time_us": 6754.447000000001, + "pct_cuda_time": 92.73999863796283, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cuda_time_us": 3.488, + "pct_cuda_time": 0.04789098430252163, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", + "cuda_time_us": 3.488, + "pct_cuda_time": 0.04789098430252163, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cuda_time_us": 6747.855, + "pct_cuda_time": 92.64948907129933, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cuda_time_us": 198.366, + "pct_cuda_time": 2.723607509218465, + "invocations": 64 + }, + "children": [ + { + "entry": { + "name": "void vllm::rms_norm_kernel(c10::BFloat16*, c10::BFloat16 const*, c10::BFloat16 const*, float, int, int)", + "cuda_time_us": 4.48, + "pct_cuda_time": 0.061511355984890166, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cuda_time_us": 193.886, + "pct_cuda_time": 2.6620961532335743, + "invocations": 63 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cuda_time_us": 2054.477, + "pct_cuda_time": 28.208407613787763, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cuda_time_us": 721.622, + "pct_cuda_time": 9.908024046546519, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cuda_time_us": 721.622, + "pct_cuda_time": 9.908024046546519, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cuda_time_us": 119.233, + "pct_cuda_time": 1.637094533068395, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cuda_time_us": 119.233, + "pct_cuda_time": 1.637094533068395, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cuda_time_us": 685.7250000000001, + "pct_cuda_time": 9.4151505765042, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cuda_time_us": 80.995, + "pct_cuda_time": 1.11207863348129, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cuda_time_us": 562.618, + "pct_cuda_time": 7.724865196764941, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cuda_time_us": 42.111999999999995, + "pct_cuda_time": 0.5782067462579674, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cuda_time_us": 527.8969999999999, + "pct_cuda_time": 7.248138457668651, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cuda_time_us": 527.8969999999999, + "pct_cuda_time": 7.248138457668651, + "invocations": 32 + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cuda_time_us": 4495.012000000001, + "pct_cuda_time": 61.71747394829311, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cuda_time_us": 2709.624, + "pct_cuda_time": 37.2037157252683, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cuda_time_us": 2709.624, + "pct_cuda_time": 37.2037157252683, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cuda_time_us": 292.03000000000003, + "pct_cuda_time": 4.009634216131134, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cuda_time_us": 292.03000000000003, + "pct_cuda_time": 4.009634216131134, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cuda_time_us": 1493.358, + "pct_cuda_time": 20.50412400689366, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cuda_time_us": 1414.609, + "pct_cuda_time": 19.422883432685154, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cuda_time_us": 78.74900000000002, + "pct_cuda_time": 1.081240574208508, + "invocations": 32 + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "invocations": 1 + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cuda_time_us": 389.915, + "pct_cuda_time": 5.3536161537608145, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04437604967481362, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cuda_time_us": 0.736, + "pct_cuda_time": 0.010105437054660526, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cuda_time_us": 385.947, + "pct_cuda_time": 5.29913466703134, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cuda_time_us": 138.846, + "pct_cuda_time": 1.9063852082763528, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cuda_time_us": 5.342999999999999, + "pct_cuda_time": 0.07336053014001519, + "invocations": 7 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cuda_time_us": 9.28, + "pct_cuda_time": 0.12741638025441535, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::elementwise_kernel<128, 4, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1})", + "cuda_time_us": 13.216, + "pct_cuda_time": 0.18145850015542597, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::SoftMaxForwardEpilogue>(float*, float const*, int)", + "cuda_time_us": 35.392, + "pct_cuda_time": 0.4859397122806323, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::LogSoftMaxForwardEpilogue>(float*, float const*, int)", + "cuda_time_us": 29.791, + "pct_cuda_time": 0.40903678708613006, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cuda_time_us": 2.112, + "pct_cuda_time": 0.02899821067859108, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::index_elementwise_kernel<128, 4, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1}>(long, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1})", + "cuda_time_us": 12.992, + "pct_cuda_time": 0.1783829323561815, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, long, 4> >(at::native::ReduceOp, unsigned int, long, 4>)", + "cuda_time_us": 28.223, + "pct_cuda_time": 0.3875078124914185, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cuda_time_us": 2.497, + "pct_cuda_time": 0.03428434283354257, + "invocations": 1 + }, + "children": [] + } + ] + } + ], + "model_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cpu_time_us": 96341.781, + "cuda_time_us": 6754.447000000001, + "pct_cuda_time": 92.73999863796283, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cpu_time_us": 385.689, + "cuda_time_us": 3.488, + "pct_cuda_time": 0.04789098430252163, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 3.488, + "pct_cuda_time": 0.04789098430252163, + "trace": "index_select(bfloat16[128256, 4096], 0, int64[20]) <- embedding(bfloat16[128256, 4096], int64[20], -1, False, False)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 11236.205, + "cuda_time_us": 219.64499999999998, + "pct_cuda_time": 3.0157727199333033, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 319.938, + "cuda_time_us": 4.48, + "pct_cuda_time": 0.061511355984890166, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rms_norm_kernel(c10::BFloat16*, c10::BFloat16 const*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 4.48, + "pct_cuda_time": 0.061511355984890166, + "trace": "_C::rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 9718.282, + "cuda_time_us": 72.15899999999999, + "pct_cuda_time": 0.9907584679718054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 741.019, + "cuda_time_us": 28.159, + "pct_cuda_time": 0.3866290788344915, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 28.159, + "pct_cuda_time": 0.3866290788344915, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 1444.956, + "cuda_time_us": 3.584, + "pct_cuda_time": 0.04920908478791213, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.584, + "pct_cuda_time": 0.04920908478791213, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1589.362, + "cuda_time_us": 22.304, + "pct_cuda_time": 0.3062386794390603, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.03339187896322609, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 18.496, + "pct_cuda_time": 0.25395402685190366, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.376, + "pct_cuda_time": 0.018892773623930548, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 542.896, + "cuda_time_us": 18.112, + "pct_cuda_time": 0.24868162491034165, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 18.112, + "pct_cuda_time": 0.24868162491034165, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 181.062, + "cuda_time_us": 2.816, + "pct_cuda_time": 0.0386642809047881, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.816, + "pct_cuda_time": 0.0386642809047881, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 871.656, + "cuda_time_us": 140.19, + "pct_cuda_time": 1.9248386150718195, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 299.72, + "cuda_time_us": 84.383, + "pct_cuda_time": 1.158596596444863, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.383, + "pct_cuda_time": 1.158596596444863, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 198.741, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.12609827976902482, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.12609827976902482, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 275.032, + "cuda_time_us": 46.623000000000005, + "pct_cuda_time": 0.6401437388579317, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.383, + "pct_cuda_time": 0.6093880608654867, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.24, + "pct_cuda_time": 0.030755677992445083, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2975.749, + "cuda_time_us": 212.096, + "pct_cuda_time": 2.9121233390560857, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 126.623, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2138.408, + "cuda_time_us": 63.104, + "pct_cuda_time": 0.8664313857300243, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 168.575, + "cuda_time_us": 21.792, + "pct_cuda_time": 0.2992088101836443, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.792, + "pct_cuda_time": 0.2992088101836443, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 581.397, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.051845285758693134, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.051845285758693134, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 957.109, + "cuda_time_us": 21.215999999999998, + "pct_cuda_time": 0.29130020727130124, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033831245791689585, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.439, + "pct_cuda_time": 0.23944119129921865, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.313, + "pct_cuda_time": 0.01802777018039303, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 201.281, + "cuda_time_us": 16.32, + "pct_cuda_time": 0.2240770825163856, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.32, + "pct_cuda_time": 0.2240770825163856, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 106.417, + "cuda_time_us": 2.848, + "pct_cuda_time": 0.039103647733251604, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.848, + "pct_cuda_time": 0.039103647733251604, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 505.961, + "cuda_time_us": 143.04, + "pct_cuda_time": 1.9639697232318503, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 148.898, + "cuda_time_us": 87.679, + "pct_cuda_time": 1.2038513797766037, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 87.679, + "pct_cuda_time": 1.2038513797766037, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 124.629, + "cuda_time_us": 8.896, + "pct_cuda_time": 0.12214397831285333, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.896, + "pct_cuda_time": 0.12214397831285333, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.436, + "cuda_time_us": 46.464999999999996, + "pct_cuda_time": 0.6379743651423931, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.032, + "pct_cuda_time": 0.6045687559657775, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.433, + "pct_cuda_time": 0.03340560917661557, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2677.831, + "cuda_time_us": 211.195, + "pct_cuda_time": 2.8997524167921602, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.878, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.043057949189423114, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.043057949189423114, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1938.302, + "cuda_time_us": 64.50999999999999, + "pct_cuda_time": 0.8857360657556392, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 163.239, + "cuda_time_us": 21.791, + "pct_cuda_time": 0.29919507997025485, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.791, + "pct_cuda_time": 0.29919507997025485, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 541.895, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.05140591893022964, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.05140591893022964, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 848.764, + "cuda_time_us": 22.208, + "pct_cuda_time": 0.3049205789536698, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033831245791689585, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 18.368, + "pct_cuda_time": 0.25219655953804965, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.376, + "pct_cuda_time": 0.018892773623930548, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 195.55, + "cuda_time_us": 16.767, + "pct_cuda_time": 0.23021448790148513, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.767, + "pct_cuda_time": 0.23021448790148513, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.964, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 489.266, + "cuda_time_us": 140.541, + "pct_cuda_time": 1.9296579199715287, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.936, + "cuda_time_us": 84.638, + "pct_cuda_time": 1.1620978008591818, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.638, + "pct_cuda_time": 1.1620978008591818, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.101, + "cuda_time_us": 9.216, + "pct_cuda_time": 0.1265376465974883, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.216, + "pct_cuda_time": 0.1265376465974883, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 168.468, + "cuda_time_us": 46.687000000000005, + "pct_cuda_time": 0.6410224725148588, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.255, + "pct_cuda_time": 0.6076305935516326, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.03339187896322609, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2749.335, + "cuda_time_us": 211.233, + "pct_cuda_time": 2.900274164900961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.817, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.042632312574349095, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.042632312574349095, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1976.84, + "cuda_time_us": 64.416, + "pct_cuda_time": 0.8844454256970278, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 181.32, + "cuda_time_us": 22.816, + "pct_cuda_time": 0.31326854869447635, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.816, + "pct_cuda_time": 0.31326854869447635, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 512.233, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.049648451616375634, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.049648451616375634, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 859.784, + "cuda_time_us": 21.568, + "pct_cuda_time": 0.2961332423843998, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.03427061262015309, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.792, + "pct_cuda_time": 0.24428795662570665, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.017574673138540046, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 222.421, + "cuda_time_us": 16.416, + "pct_cuda_time": 0.22539518300177608, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.416, + "pct_cuda_time": 0.22539518300177608, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 91.848, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 510.01, + "cuda_time_us": 140.704, + "pct_cuda_time": 1.9318959447540147, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 176.85, + "cuda_time_us": 85.023, + "pct_cuda_time": 1.1673839330141331, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 85.023, + "pct_cuda_time": 1.1673839330141331, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.857, + "cuda_time_us": 8.961, + "pct_cuda_time": 0.12303644218316981, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.961, + "pct_cuda_time": 0.12303644218316981, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 167.464, + "cuda_time_us": 46.72, + "pct_cuda_time": 0.6414755695567117, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.32, + "pct_cuda_time": 0.6085230574219491, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.4, + "pct_cuda_time": 0.032952512134762586, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2704.155, + "cuda_time_us": 209.534, + "pct_cuda_time": 2.8769465323522265, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.553, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1934.864, + "cuda_time_us": 64.128, + "pct_cuda_time": 0.8804911242408564, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 172.735, + "cuda_time_us": 22.016, + "pct_cuda_time": 0.30228437798288876, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.016, + "pct_cuda_time": 0.30228437798288876, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 501.397, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.050966552101766135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.050966552101766135, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 867.429, + "cuda_time_us": 21.472, + "pct_cuda_time": 0.2948151418990093, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.592, + "pct_cuda_time": 0.035588713105543596, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.6, + "pct_cuda_time": 0.24165175565492567, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.017574673138540046, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 199.474, + "cuda_time_us": 16.928, + "pct_cuda_time": 0.23242505225719215, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.928, + "pct_cuda_time": 0.23242505225719215, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.214, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 527.417, + "cuda_time_us": 139.294, + "pct_cuda_time": 1.912536343874842, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 174.931, + "cuda_time_us": 84.382, + "pct_cuda_time": 1.1585828662314737, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.382, + "pct_cuda_time": 1.1585828662314737, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 127.192, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.12434081245517083, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.12434081245517083, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 165.333, + "cuda_time_us": 45.856, + "pct_cuda_time": 0.6296126651881973, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.456, + "pct_cuda_time": 0.5966601530534346, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.4, + "pct_cuda_time": 0.032952512134762586, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2889.659, + "cuda_time_us": 211.902, + "pct_cuda_time": 2.9094596776585253, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.462, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.043057949189423114, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.043057949189423114, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2103.081, + "cuda_time_us": 64.863, + "pct_cuda_time": 0.8905828310821274, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.407, + "cuda_time_us": 23.391, + "pct_cuda_time": 0.32116342139342985, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 23.391, + "pct_cuda_time": 0.32116342139342985, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 496.497, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05272401941562014, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05272401941562014, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1050.905, + "cuda_time_us": 21.312, + "pct_cuda_time": 0.2926183077566918, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.592, + "pct_cuda_time": 0.035588713105543596, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.408, + "pct_cuda_time": 0.23901555468414465, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 214.43, + "cuda_time_us": 16.32, + "pct_cuda_time": 0.2240770825163856, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.32, + "pct_cuda_time": 0.2240770825163856, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.185, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 540.021, + "cuda_time_us": 140.89499999999998, + "pct_cuda_time": 1.934518415511406, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 177.423, + "cuda_time_us": 84.767, + "pct_cuda_time": 1.1638689983864252, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.767, + "pct_cuda_time": 1.1638689983864252, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 132.062, + "cuda_time_us": 9.408, + "pct_cuda_time": 0.12917384756826933, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.408, + "pct_cuda_time": 0.12917384756826933, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 169.76, + "cuda_time_us": 46.72, + "pct_cuda_time": 0.6414755695567117, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.32, + "pct_cuda_time": 0.6085230574219491, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.4, + "pct_cuda_time": 0.032952512134762586, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2666.512, + "cuda_time_us": 210.84300000000002, + "pct_cuda_time": 2.894919381679062, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.58, + "cuda_time_us": 3.296, + "pct_cuda_time": 0.045254783331740614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.296, + "pct_cuda_time": 0.045254783331740614, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1914.455, + "cuda_time_us": 63.614000000000004, + "pct_cuda_time": 0.8734337945586614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 166.366, + "cuda_time_us": 21.983, + "pct_cuda_time": 0.30183128094103584, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.983, + "pct_cuda_time": 0.30183128094103584, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 547.268, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.051845285758693134, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.051845285758693134, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 830.045, + "cuda_time_us": 21.408, + "pct_cuda_time": 0.2939364082420823, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.56, + "pct_cuda_time": 0.03514934627708009, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.536, + "pct_cuda_time": 0.24077302199799863, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 201.056, + "cuda_time_us": 16.447, + "pct_cuda_time": 0.2258208196168501, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.447, + "pct_cuda_time": 0.2258208196168501, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 102.904, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 496.246, + "cuda_time_us": 140.925, + "pct_cuda_time": 1.934930321913091, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 178.583, + "cuda_time_us": 85.055, + "pct_cuda_time": 1.1678232998425968, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 85.055, + "pct_cuda_time": 1.1678232998425968, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.206, + "cuda_time_us": 9.503, + "pct_cuda_time": 0.13047821784027036, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.503, + "pct_cuda_time": 0.13047821784027036, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 165.002, + "cuda_time_us": 46.367000000000004, + "pct_cuda_time": 0.6366288042302237, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.904, + "pct_cuda_time": 0.6028112886519236, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.463, + "pct_cuda_time": 0.0338175155783001, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2552.352, + "cuda_time_us": 210.334, + "pct_cuda_time": 2.887930703063814, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.203, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04217921553249611, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04217921553249611, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1823.323, + "cuda_time_us": 64.384, + "pct_cuda_time": 0.8840060588685644, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 161.932, + "cuda_time_us": 22.912, + "pct_cuda_time": 0.31458664917986684, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.912, + "pct_cuda_time": 0.31458664917986684, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 497.497, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.051845285758693134, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.051845285758693134, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 830.861, + "cuda_time_us": 21.408, + "pct_cuda_time": 0.2939364082420823, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033831245791689585, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.632, + "pct_cuda_time": 0.24209112248338915, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 170.162, + "cuda_time_us": 16.288, + "pct_cuda_time": 0.2236377156879221, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.288, + "pct_cuda_time": 0.2236377156879221, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.106, + "cuda_time_us": 2.975, + "pct_cuda_time": 0.040847384833716124, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.975, + "pct_cuda_time": 0.040847384833716124, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 484.96, + "cuda_time_us": 139.903, + "pct_cuda_time": 1.9208980438290373, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.551, + "cuda_time_us": 84.607, + "pct_cuda_time": 1.1616721642441077, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.607, + "pct_cuda_time": 1.1616721642441077, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 91.857, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.12434081245517083, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.12434081245517083, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 164.186, + "cuda_time_us": 46.24, + "pct_cuda_time": 0.6348850671297592, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.84, + "pct_cuda_time": 0.6019325549949966, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.4, + "pct_cuda_time": 0.032952512134762586, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2622.395, + "cuda_time_us": 208.829, + "pct_cuda_time": 2.86726673191264, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.533, + "cuda_time_us": 3.296, + "pct_cuda_time": 0.045254783331740614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.296, + "pct_cuda_time": 0.045254783331740614, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1880.325, + "cuda_time_us": 63.39, + "pct_cuda_time": 0.8703582267594169, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 157.028, + "cuda_time_us": 22.368, + "pct_cuda_time": 0.3071174130959873, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.368, + "pct_cuda_time": 0.3071174130959873, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 495.786, + "cuda_time_us": 3.775, + "pct_cuda_time": 0.05183155554530365, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.775, + "pct_cuda_time": 0.05183155554530365, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 828.92, + "cuda_time_us": 21.151, + "pct_cuda_time": 0.2904077434009848, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.56, + "pct_cuda_time": 0.03514934627708009, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.311, + "pct_cuda_time": 0.23768372398536466, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.017574673138540046, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 216.09, + "cuda_time_us": 16.096, + "pct_cuda_time": 0.2210015147171411, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.096, + "pct_cuda_time": 0.2210015147171411, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.833, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04217921553249611, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04217921553249611, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 506.865, + "cuda_time_us": 139.071, + "pct_cuda_time": 1.9094745062889866, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 171.57, + "cuda_time_us": 83.839, + "pct_cuda_time": 1.1511273603609835, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.839, + "pct_cuda_time": 1.1511273603609835, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 109.113, + "cuda_time_us": 8.832, + "pct_cuda_time": 0.12126524465592634, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.832, + "pct_cuda_time": 0.12126524465592634, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 143.876, + "cuda_time_us": 46.4, + "pct_cuda_time": 0.6370819012720766, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.936, + "pct_cuda_time": 0.6032506554803871, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033831245791689585, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2687.821, + "cuda_time_us": 211.101, + "pct_cuda_time": 2.8984617767335488, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.798, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.043057949189423114, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.043057949189423114, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1960.513, + "cuda_time_us": 63.838, + "pct_cuda_time": 0.8765093623579058, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 263.591, + "cuda_time_us": 22.655, + "pct_cuda_time": 0.31105798433876936, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.655, + "pct_cuda_time": 0.31105798433876936, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 488.823, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05008781844483913, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05008781844483913, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 814.482, + "cuda_time_us": 20.992, + "pct_cuda_time": 0.2882246394720568, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.03339187896322609, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.248, + "pct_cuda_time": 0.23681872054182712, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 201.198, + "cuda_time_us": 16.543, + "pct_cuda_time": 0.22713892010224063, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.543, + "pct_cuda_time": 0.22713892010224063, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.356, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.043057949189423114, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.043057949189423114, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 486.238, + "cuda_time_us": 140.99099999999999, + "pct_cuda_time": 1.9358365159967965, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 171.673, + "cuda_time_us": 85.247, + "pct_cuda_time": 1.1704595008133776, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 85.247, + "pct_cuda_time": 1.1704595008133776, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.318, + "cuda_time_us": 9.216, + "pct_cuda_time": 0.1265376465974883, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.216, + "pct_cuda_time": 0.1265376465974883, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 164.467, + "cuda_time_us": 46.528000000000006, + "pct_cuda_time": 0.6388393685859308, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.968, + "pct_cuda_time": 0.6036900223088506, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.56, + "pct_cuda_time": 0.03514934627708009, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2517.951, + "cuda_time_us": 210.39800000000002, + "pct_cuda_time": 2.8888094367207415, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.028, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04217921553249611, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04217921553249611, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1804.482, + "cuda_time_us": 63.906000000000006, + "pct_cuda_time": 0.8774430168683909, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 181.637, + "cuda_time_us": 21.92, + "pct_cuda_time": 0.3009662774974983, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.92, + "pct_cuda_time": 0.3009662774974983, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 477.131, + "cuda_time_us": 3.713, + "pct_cuda_time": 0.05098028231515562, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.713, + "pct_cuda_time": 0.05098028231515562, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 793.888, + "cuda_time_us": 21.313000000000002, + "pct_cuda_time": 0.2926320379700813, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.497, + "pct_cuda_time": 0.03428434283354257, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.504, + "pct_cuda_time": 0.24033365516953514, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 189.701, + "cuda_time_us": 16.96, + "pct_cuda_time": 0.2328644190856556, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.96, + "pct_cuda_time": 0.2328644190856556, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.982, + "cuda_time_us": 2.912, + "pct_cuda_time": 0.0399823813901786, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.912, + "pct_cuda_time": 0.0399823813901786, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 465.194, + "cuda_time_us": 140.508, + "pct_cuda_time": 1.9292048229296759, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.437, + "cuda_time_us": 84.158, + "pct_cuda_time": 1.1555072984322292, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.158, + "pct_cuda_time": 1.1555072984322292, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.391, + "cuda_time_us": 9.248, + "pct_cuda_time": 0.12697701342595183, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.248, + "pct_cuda_time": 0.12697701342595183, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.318, + "cuda_time_us": 47.102, + "pct_cuda_time": 0.6467205110714946, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.479, + "pct_cuda_time": 0.6107061613508771, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.623, + "pct_cuda_time": 0.036014349720617615, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2710.142, + "cuda_time_us": 212.029, + "pct_cuda_time": 2.9112034147589902, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.498, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04349731601788662, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04349731601788662, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1908.431, + "cuda_time_us": 64.511, + "pct_cuda_time": 0.8857497959690288, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.814, + "cuda_time_us": 22.912, + "pct_cuda_time": 0.31458664917986684, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.912, + "pct_cuda_time": 0.31458664917986684, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 505.86, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05052718527330264, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05052718527330264, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 812.096, + "cuda_time_us": 21.439, + "pct_cuda_time": 0.2943620448571563, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.03427061262015309, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.664, + "pct_cuda_time": 0.24253048931185267, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.279, + "pct_cuda_time": 0.017560942925150563, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 199.798, + "cuda_time_us": 16.48, + "pct_cuda_time": 0.2262739166587031, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.48, + "pct_cuda_time": 0.2262739166587031, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 90.959, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 542.099, + "cuda_time_us": 141.342, + "pct_cuda_time": 1.940655820896506, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.955, + "cuda_time_us": 85.311, + "pct_cuda_time": 1.1713382344703047, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 85.311, + "pct_cuda_time": 1.1713382344703047, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.793, + "cuda_time_us": 9.408, + "pct_cuda_time": 0.12917384756826933, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.408, + "pct_cuda_time": 0.12917384756826933, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 223.241, + "cuda_time_us": 46.623000000000005, + "pct_cuda_time": 0.6401437388579317, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.191, + "pct_cuda_time": 0.6067518598947057, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.03339187896322609, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2727.391, + "cuda_time_us": 210.363, + "pct_cuda_time": 2.888328879252109, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.651, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04217921553249611, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04217921553249611, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1991.456, + "cuda_time_us": 63.772999999999996, + "pct_cuda_time": 0.8756168984875893, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 166.188, + "cuda_time_us": 21.983, + "pct_cuda_time": 0.30183128094103584, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.983, + "pct_cuda_time": 0.30183128094103584, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 503.172, + "cuda_time_us": 3.936, + "pct_cuda_time": 0.05404211990101064, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.936, + "pct_cuda_time": 0.05404211990101064, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 937.749, + "cuda_time_us": 21.471, + "pct_cuda_time": 0.2948014116856198, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.56, + "pct_cuda_time": 0.03514934627708009, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.631, + "pct_cuda_time": 0.24207739226999964, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.017574673138540046, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 183.649, + "cuda_time_us": 16.383, + "pct_cuda_time": 0.2249420859599231, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.383, + "pct_cuda_time": 0.2249420859599231, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.576, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.04086111504710561, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.04086111504710561, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 496.901, + "cuda_time_us": 140.542, + "pct_cuda_time": 1.9296716501849183, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.667, + "cuda_time_us": 84.958, + "pct_cuda_time": 1.1664914691438166, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.958, + "pct_cuda_time": 1.1664914691438166, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 107.334, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.12390144562670732, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.12390144562670732, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 165.828, + "cuda_time_us": 46.56, + "pct_cuda_time": 0.6392787354143942, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.128, + "pct_cuda_time": 0.605886856451168, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.03339187896322609, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2547.8, + "cuda_time_us": 212.66900000000004, + "pct_cuda_time": 2.919990751328261, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.57, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04349731601788662, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04349731601788662, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1822.332, + "cuda_time_us": 64.543, + "pct_cuda_time": 0.8861891627974924, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 161.928, + "cuda_time_us": 22.911, + "pct_cuda_time": 0.3145729189664774, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.911, + "pct_cuda_time": 0.3145729189664774, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 484.511, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05052718527330264, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05052718527330264, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 824.624, + "cuda_time_us": 21.791, + "pct_cuda_time": 0.29919507997025485, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.624, + "pct_cuda_time": 0.0360280799340071, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.855, + "pct_cuda_time": 0.24515296006924417, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 195.127, + "cuda_time_us": 16.161, + "pct_cuda_time": 0.2218939785874576, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.161, + "pct_cuda_time": 0.2218939785874576, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.84, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04173984870403261, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04173984870403261, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 491.812, + "cuda_time_us": 141.918, + "pct_cuda_time": 1.9485644238088486, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.027, + "cuda_time_us": 84.863, + "pct_cuda_time": 1.1651870988718156, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.863, + "pct_cuda_time": 1.1651870988718156, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.471, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.12390144562670732, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.12390144562670732, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 163.2, + "cuda_time_us": 48.031, + "pct_cuda_time": 0.6594758793103258, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 45.535, + "pct_cuda_time": 0.6252052666901726, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.03427061262015309, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2651.469, + "cuda_time_us": 210.33299999999997, + "pct_cuda_time": 2.8879169728504244, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.23, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1919.231, + "cuda_time_us": 63.870999999999995, + "pct_cuda_time": 0.8769624593997587, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 174.167, + "cuda_time_us": 21.984, + "pct_cuda_time": 0.3018450111544253, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.984, + "pct_cuda_time": 0.3018450111544253, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 527.579, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.05140591893022964, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.05140591893022964, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 848.561, + "cuda_time_us": 21.727, + "pct_cuda_time": 0.29831634631332776, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.03470997944861659, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.728, + "pct_cuda_time": 0.24340922296877968, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.471, + "pct_cuda_time": 0.02019714389593157, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 179.702, + "cuda_time_us": 16.416, + "pct_cuda_time": 0.22539518300177608, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.416, + "pct_cuda_time": 0.22539518300177608, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.564, + "cuda_time_us": 2.848, + "pct_cuda_time": 0.039103647733251604, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.848, + "pct_cuda_time": 0.039103647733251604, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 500.928, + "cuda_time_us": 140.51, + "pct_cuda_time": 1.9292322833564544, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 162.942, + "cuda_time_us": 84.671, + "pct_cuda_time": 1.1625508979010346, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.671, + "pct_cuda_time": 1.1625508979010346, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 113.175, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.12609827976902482, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.12609827976902482, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 164.04, + "cuda_time_us": 46.654999999999994, + "pct_cuda_time": 0.6405831056863951, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.224, + "pct_cuda_time": 0.6072049569365585, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.431, + "pct_cuda_time": 0.033378148749836606, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2503.445, + "cuda_time_us": 210.079, + "pct_cuda_time": 2.884429498649496, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.476, + "cuda_time_us": 3.264, + "pct_cuda_time": 0.04481541650327712, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.264, + "pct_cuda_time": 0.04481541650327712, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1796.64, + "cuda_time_us": 64.19300000000001, + "pct_cuda_time": 0.8813835881111729, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 152.485, + "cuda_time_us": 22.624, + "pct_cuda_time": 0.3106323477236953, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.624, + "pct_cuda_time": 0.3106323477236953, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 481.428, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05008781844483913, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05008781844483913, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 802.723, + "cuda_time_us": 21.345000000000002, + "pct_cuda_time": 0.29307140479854477, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.657, + "pct_cuda_time": 0.036481176975860084, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.28, + "pct_cuda_time": 0.23725808737029064, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.408, + "pct_cuda_time": 0.01933214045239405, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 187.279, + "cuda_time_us": 16.576, + "pct_cuda_time": 0.2275920171440936, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.576, + "pct_cuda_time": 0.2275920171440936, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.113, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.04086111504710561, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.04086111504710561, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 484.968, + "cuda_time_us": 139.64600000000002, + "pct_cuda_time": 1.9173693789879405, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 154.821, + "cuda_time_us": 84.031, + "pct_cuda_time": 1.1537635613317645, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.031, + "pct_cuda_time": 1.1537635613317645, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.449, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.12609827976902482, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.12609827976902482, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 177.014, + "cuda_time_us": 46.431000000000004, + "pct_cuda_time": 0.6375075378871508, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.999, + "pct_cuda_time": 0.6041156589239247, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.03339187896322609, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2751.569, + "cuda_time_us": 210.591, + "pct_cuda_time": 2.8914593679049116, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.623, + "cuda_time_us": 3.328, + "pct_cuda_time": 0.04569415016020412, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.328, + "pct_cuda_time": 0.04569415016020412, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1993.717, + "cuda_time_us": 63.167, + "pct_cuda_time": 0.8672963891735618, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 174.184, + "cuda_time_us": 21.727, + "pct_cuda_time": 0.29831634631332776, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.727, + "pct_cuda_time": 0.29831634631332776, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 511.292, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.05140591893022964, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.05140591893022964, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 908.263, + "cuda_time_us": 21.376, + "pct_cuda_time": 0.29349704141361876, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.03470997944861659, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.568, + "pct_cuda_time": 0.24121238882646215, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.017574673138540046, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 203.351, + "cuda_time_us": 16.32, + "pct_cuda_time": 0.2240770825163856, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.32, + "pct_cuda_time": 0.2240770825163856, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 93.11, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04217921553249611, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04217921553249611, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 513.433, + "cuda_time_us": 141.024, + "pct_cuda_time": 1.9362896130386498, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 172.675, + "cuda_time_us": 84.863, + "pct_cuda_time": 1.1651870988718156, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.863, + "pct_cuda_time": 1.1651870988718156, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 107.552, + "cuda_time_us": 8.993, + "pct_cuda_time": 0.12347580901163331, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.993, + "pct_cuda_time": 0.12347580901163331, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 172.841, + "cuda_time_us": 47.168, + "pct_cuda_time": 0.6476267051552007, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.704, + "pct_cuda_time": 0.6137954593635112, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033831245791689585, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2639.67, + "cuda_time_us": 210.30200000000002, + "pct_cuda_time": 2.887491336235351, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.254, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1846.565, + "cuda_time_us": 64.19200000000001, + "pct_cuda_time": 0.8813698578977834, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 164.164, + "cuda_time_us": 22.848, + "pct_cuda_time": 0.3137079155229398, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.848, + "pct_cuda_time": 0.3137079155229398, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 505.017, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.05140591893022964, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.05140591893022964, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 812.073, + "cuda_time_us": 21.152, + "pct_cuda_time": 0.29042147361437426, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.03427061262015309, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.344, + "pct_cuda_time": 0.23813682102721764, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 175.451, + "cuda_time_us": 16.448, + "pct_cuda_time": 0.2258345498302396, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.448, + "pct_cuda_time": 0.2258345498302396, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.353, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.04086111504710561, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.04086111504710561, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 534.433, + "cuda_time_us": 140.03, + "pct_cuda_time": 1.9226417809295022, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 166.613, + "cuda_time_us": 84.382, + "pct_cuda_time": 1.1585828662314737, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.382, + "pct_cuda_time": 1.1585828662314737, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 120.301, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.12521954611209782, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.12521954611209782, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 188.871, + "cuda_time_us": 46.528, + "pct_cuda_time": 0.6388393685859307, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.096, + "pct_cuda_time": 0.6054474896227046, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.03339187896322609, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2630.273, + "cuda_time_us": 209.791, + "pct_cuda_time": 2.880475197193324, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.241, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1864.663, + "cuda_time_us": 63.392, + "pct_cuda_time": 0.8703856871861959, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 163.463, + "cuda_time_us": 21.984, + "pct_cuda_time": 0.3018450111544253, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.984, + "pct_cuda_time": 0.3018450111544253, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 481.381, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05272401941562014, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05272401941562014, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 840.556, + "cuda_time_us": 21.312, + "pct_cuda_time": 0.2926183077566918, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.03470997944861659, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.472, + "pct_cuda_time": 0.23989428834107163, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 202.838, + "cuda_time_us": 16.256, + "pct_cuda_time": 0.2231983488594586, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.256, + "pct_cuda_time": 0.2231983488594586, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.906, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 522.854, + "cuda_time_us": 140.28699999999998, + "pct_cuda_time": 1.9261704457705993, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.978, + "cuda_time_us": 84.19, + "pct_cuda_time": 1.1559466652606927, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.19, + "pct_cuda_time": 1.1559466652606927, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 120.697, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.12434081245517083, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.12434081245517083, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 176.867, + "cuda_time_us": 47.041, + "pct_cuda_time": 0.6458829680547362, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.576, + "pct_cuda_time": 0.6120379920496571, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.465, + "pct_cuda_time": 0.03384497600507907, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2892.723, + "cuda_time_us": 210.077, + "pct_cuda_time": 2.8844020382227167, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.076, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04217921553249611, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04217921553249611, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1937.397, + "cuda_time_us": 64.768, + "pct_cuda_time": 0.8892784608101263, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 147.54, + "cuda_time_us": 23.392, + "pct_cuda_time": 0.3211771516068193, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 23.392, + "pct_cuda_time": 0.3211771516068193, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 494.532, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05008781844483913, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05008781844483913, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 916.256, + "cuda_time_us": 21.056, + "pct_cuda_time": 0.28910337312898376, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033831245791689585, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.28, + "pct_cuda_time": 0.23725808737029064, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 180.551, + "cuda_time_us": 16.672, + "pct_cuda_time": 0.2289101176294841, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.672, + "pct_cuda_time": 0.2289101176294841, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 101.149, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.04086111504710561, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.04086111504710561, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 692.488, + "cuda_time_us": 139.261, + "pct_cuda_time": 1.9120832468329887, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 169.584, + "cuda_time_us": 84.223, + "pct_cuda_time": 1.1563997623025455, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.223, + "pct_cuda_time": 1.1563997623025455, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.777, + "cuda_time_us": 8.991, + "pct_cuda_time": 0.12344834858485434, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.991, + "pct_cuda_time": 0.12344834858485434, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 363.228, + "cuda_time_us": 46.047000000000004, + "pct_cuda_time": 0.6322351359455888, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.615, + "pct_cuda_time": 0.5988432569823626, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.03339187896322609, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2724.532, + "cuda_time_us": 208.12800000000001, + "pct_cuda_time": 2.8576418523266116, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.714, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04437604967481362, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04437604967481362, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1931.091, + "cuda_time_us": 63.104000000000006, + "pct_cuda_time": 0.8664313857300244, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 171.535, + "cuda_time_us": 21.92, + "pct_cuda_time": 0.3009662774974983, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.92, + "pct_cuda_time": 0.3009662774974983, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 505.984, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.051845285758693134, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.051845285758693134, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 840.558, + "cuda_time_us": 21.216, + "pct_cuda_time": 0.2913002072713013, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.03470997944861659, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.408, + "pct_cuda_time": 0.23901555468414465, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.017574673138540046, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 212.926, + "cuda_time_us": 16.192, + "pct_cuda_time": 0.22231961520253157, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.192, + "pct_cuda_time": 0.22231961520253157, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 111.706, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.04086111504710561, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.04086111504710561, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 525.934, + "cuda_time_us": 138.816, + "pct_cuda_time": 1.905973301874668, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 176.965, + "cuda_time_us": 84.191, + "pct_cuda_time": 1.155960395474082, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.191, + "pct_cuda_time": 1.155960395474082, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 129.713, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.12258334514131683, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.12258334514131683, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 156.043, + "cuda_time_us": 45.697, + "pct_cuda_time": 0.6274295612592692, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.232, + "pct_cuda_time": 0.59358458525419, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.465, + "pct_cuda_time": 0.03384497600507907, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2639.772, + "cuda_time_us": 211.26000000000002, + "pct_cuda_time": 2.900644880662477, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.746, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04217921553249611, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04217921553249611, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1873.419, + "cuda_time_us": 64.095, + "pct_cuda_time": 0.8800380271990033, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 164.026, + "cuda_time_us": 22.848, + "pct_cuda_time": 0.3137079155229398, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.848, + "pct_cuda_time": 0.3137079155229398, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 491.507, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05008781844483913, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05008781844483913, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 837.954, + "cuda_time_us": 21.28, + "pct_cuda_time": 0.29217894092822827, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.03427061262015309, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.504, + "pct_cuda_time": 0.24033365516953514, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.017574673138540046, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 191.99, + "cuda_time_us": 16.319, + "pct_cuda_time": 0.2240633523029961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.319, + "pct_cuda_time": 0.2240633523029961, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 105.939, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04349731601788662, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04349731601788662, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 498.988, + "cuda_time_us": 140.925, + "pct_cuda_time": 1.934930321913091, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 142.976, + "cuda_time_us": 84.575, + "pct_cuda_time": 1.1612327974156442, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.575, + "pct_cuda_time": 1.1612327974156442, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 123.996, + "cuda_time_us": 9.248, + "pct_cuda_time": 0.12697701342595183, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.248, + "pct_cuda_time": 0.12697701342595183, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 169.507, + "cuda_time_us": 47.102000000000004, + "pct_cuda_time": 0.6467205110714948, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.639, + "pct_cuda_time": 0.6129029954931947, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.463, + "pct_cuda_time": 0.0338175155783001, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2744.059, + "cuda_time_us": 211.74099999999999, + "pct_cuda_time": 2.9072491133028184, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.754, + "cuda_time_us": 3.167, + "pct_cuda_time": 0.04348358580449713, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.167, + "pct_cuda_time": 0.04348358580449713, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1985.917, + "cuda_time_us": 63.775999999999996, + "pct_cuda_time": 0.8756580891277578, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 174.866, + "cuda_time_us": 21.76, + "pct_cuda_time": 0.2987694433551808, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.76, + "pct_cuda_time": 0.2987694433551808, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 547.265, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.051845285758693134, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.051845285758693134, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 867.956, + "cuda_time_us": 21.887999999999998, + "pct_cuda_time": 0.30052691066903475, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.03470997944861659, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 18.08, + "pct_cuda_time": 0.24824225808187814, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.017574673138540046, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 226.358, + "cuda_time_us": 16.352, + "pct_cuda_time": 0.22451644934484907, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.352, + "pct_cuda_time": 0.22451644934484907, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.269, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.04086111504710561, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.04086111504710561, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 508.72, + "cuda_time_us": 141.822, + "pct_cuda_time": 1.9472463233234583, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 167.112, + "cuda_time_us": 85.503, + "pct_cuda_time": 1.1739744354410857, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 85.503, + "pct_cuda_time": 1.1739744354410857, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 114.771, + "cuda_time_us": 9.28, + "pct_cuda_time": 0.12741638025441535, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.28, + "pct_cuda_time": 0.12741638025441535, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 166.175, + "cuda_time_us": 47.039, + "pct_cuda_time": 0.6458555076279573, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.575, + "pct_cuda_time": 0.6120242618362677, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033831245791689585, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2676.907, + "cuda_time_us": 210.17399999999998, + "pct_cuda_time": 2.885733868921496, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.333, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1921.754, + "cuda_time_us": 64.128, + "pct_cuda_time": 0.8804911242408564, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 178.663, + "cuda_time_us": 22.592, + "pct_cuda_time": 0.31019298089523184, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.592, + "pct_cuda_time": 0.31019298089523184, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 499.898, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.051845285758693134, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.051845285758693134, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 852.956, + "cuda_time_us": 21.28, + "pct_cuda_time": 0.29217894092822827, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.03470997944861659, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.44, + "pct_cuda_time": 0.23945492151260817, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 197.692, + "cuda_time_us": 16.48, + "pct_cuda_time": 0.2262739166587031, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.48, + "pct_cuda_time": 0.2262739166587031, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.616, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 502.857, + "cuda_time_us": 139.934, + "pct_cuda_time": 1.9213236804441116, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 168.143, + "cuda_time_us": 83.999, + "pct_cuda_time": 1.153324194503301, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.999, + "pct_cuda_time": 1.153324194503301, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.175, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.12521954611209782, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.12521954611209782, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 170.862, + "cuda_time_us": 46.815, + "pct_cuda_time": 0.6427799398287127, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.223, + "pct_cuda_time": 0.6071912267231692, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.592, + "pct_cuda_time": 0.035588713105543596, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2556.175, + "cuda_time_us": 210.847, + "pct_cuda_time": 2.89497430253262, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.874, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.042632312574349095, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.042632312574349095, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1822.197, + "cuda_time_us": 63.425, + "pct_cuda_time": 0.8708387842280488, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 164.998, + "cuda_time_us": 22.08, + "pct_cuda_time": 0.3031631116398158, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.08, + "pct_cuda_time": 0.3031631116398158, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 462.923, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05052718527330264, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05052718527330264, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 803.938, + "cuda_time_us": 21.313000000000002, + "pct_cuda_time": 0.2926320379700813, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.529, + "pct_cuda_time": 0.03472370966200608, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.504, + "pct_cuda_time": 0.24033365516953514, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.017574673138540046, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 201.367, + "cuda_time_us": 16.352, + "pct_cuda_time": 0.22451644934484907, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.352, + "pct_cuda_time": 0.22451644934484907, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.917, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04437604967481362, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04437604967481362, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 487.997, + "cuda_time_us": 141.085, + "pct_cuda_time": 1.9371271560554082, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 167.672, + "cuda_time_us": 84.254, + "pct_cuda_time": 1.1568253989176196, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.254, + "pct_cuda_time": 1.1568253989176196, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.39, + "cuda_time_us": 9.312, + "pct_cuda_time": 0.12785574708287883, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.312, + "pct_cuda_time": 0.12785574708287883, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 162.031, + "cuda_time_us": 47.519000000000005, + "pct_cuda_time": 0.6524460100549098, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 45.087, + "pct_cuda_time": 0.6190541310916837, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.03339187896322609, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2669.749, + "cuda_time_us": 209.91500000000002, + "pct_cuda_time": 2.8821777436536204, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.283, + "cuda_time_us": 3.135, + "pct_cuda_time": 0.043044218976033624, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.135, + "pct_cuda_time": 0.043044218976033624, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1921.751, + "cuda_time_us": 63.902, + "pct_cuda_time": 0.8773880960148328, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 160.86, + "cuda_time_us": 22.751, + "pct_cuda_time": 0.31237608482415985, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.751, + "pct_cuda_time": 0.31237608482415985, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 534.116, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05008781844483913, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05008781844483913, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 843.409, + "cuda_time_us": 21.152, + "pct_cuda_time": 0.29042147361437426, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.03470997944861659, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.312, + "pct_cuda_time": 0.23769745419875413, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 174.279, + "cuda_time_us": 16.351, + "pct_cuda_time": 0.22450271913145958, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.351, + "pct_cuda_time": 0.22450271913145958, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.676, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04130048187556911, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 495.947, + "cuda_time_us": 139.87, + "pct_cuda_time": 1.9204449467871847, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.372, + "cuda_time_us": 83.807, + "pct_cuda_time": 1.15068799353252, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.807, + "pct_cuda_time": 1.15068799353252, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.086, + "cuda_time_us": 8.991, + "pct_cuda_time": 0.12344834858485434, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.991, + "pct_cuda_time": 0.12344834858485434, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 174.504, + "cuda_time_us": 47.071999999999996, + "pct_cuda_time": 0.6463086046698102, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.608, + "pct_cuda_time": 0.6124773588781206, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033831245791689585, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2614.439, + "cuda_time_us": 209.72500000000002, + "pct_cuda_time": 2.8795690031096184, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.449, + "cuda_time_us": 3.296, + "pct_cuda_time": 0.045254783331740614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.296, + "pct_cuda_time": 0.045254783331740614, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1882.953, + "cuda_time_us": 63.455, + "pct_cuda_time": 0.8712506906297333, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 161.475, + "cuda_time_us": 21.824, + "pct_cuda_time": 0.29964817701210783, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.824, + "pct_cuda_time": 0.29964817701210783, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 519.653, + "cuda_time_us": 3.872, + "pct_cuda_time": 0.05316338624408364, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.872, + "pct_cuda_time": 0.05316338624408364, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 821.429, + "cuda_time_us": 21.247, + "pct_cuda_time": 0.2917258438863753, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.03470997944861659, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.407, + "pct_cuda_time": 0.23900182447075513, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 184.833, + "cuda_time_us": 16.512, + "pct_cuda_time": 0.2267132834871666, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.512, + "pct_cuda_time": 0.2267132834871666, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.862, + "cuda_time_us": 2.944, + "pct_cuda_time": 0.040421748218642105, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.944, + "pct_cuda_time": 0.040421748218642105, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 486.611, + "cuda_time_us": 140.03, + "pct_cuda_time": 1.9226417809295022, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 162.747, + "cuda_time_us": 84.863, + "pct_cuda_time": 1.1651870988718156, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.863, + "pct_cuda_time": 1.1651870988718156, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.941, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.12609827976902482, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.12609827976902482, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 163.804, + "cuda_time_us": 45.983, + "pct_cuda_time": 0.6313564022886616, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.519, + "pct_cuda_time": 0.597525156496972, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033831245791689585, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2865.599, + "cuda_time_us": 211.169, + "pct_cuda_time": 2.8993954312440335, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.162, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2128.305, + "cuda_time_us": 64.321, + "pct_cuda_time": 0.8831410554250269, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 168.739, + "cuda_time_us": 22.752, + "pct_cuda_time": 0.3123898150375493, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.752, + "pct_cuda_time": 0.3123898150375493, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 566.07, + "cuda_time_us": 3.681, + "pct_cuda_time": 0.05054091548669212, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.681, + "pct_cuda_time": 0.05054091548669212, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1028.958, + "cuda_time_us": 21.504, + "pct_cuda_time": 0.2952545087274728, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.624, + "pct_cuda_time": 0.0360280799340071, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.568, + "pct_cuda_time": 0.24121238882646215, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 199.042, + "cuda_time_us": 16.384, + "pct_cuda_time": 0.22495581617331262, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.384, + "pct_cuda_time": 0.22495581617331262, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 88.338, + "cuda_time_us": 3.009, + "pct_cuda_time": 0.04131421208895859, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.009, + "pct_cuda_time": 0.04131421208895859, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 491.607, + "cuda_time_us": 140.735, + "pct_cuda_time": 1.9323215813690886, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.342, + "cuda_time_us": 84.927, + "pct_cuda_time": 1.1660658325287425, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.927, + "pct_cuda_time": 1.1660658325287425, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 123.825, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.12521954611209782, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.12521954611209782, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.586, + "cuda_time_us": 46.687999999999995, + "pct_cuda_time": 0.6410362027282481, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.224, + "pct_cuda_time": 0.6072049569365585, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033831245791689585, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2518.649, + "cuda_time_us": 210.812, + "pct_cuda_time": 2.894493745063988, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 95.097, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1778.113, + "cuda_time_us": 64.031, + "pct_cuda_time": 0.8791592935420764, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.283, + "cuda_time_us": 21.728, + "pct_cuda_time": 0.2983300765267173, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.728, + "pct_cuda_time": 0.2983300765267173, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 502.834, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.050966552101766135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.050966552101766135, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 770.863, + "cuda_time_us": 21.983999999999998, + "pct_cuda_time": 0.3018450111544253, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.03470997944861659, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 18.144, + "pct_cuda_time": 0.24912099173880511, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 188.894, + "cuda_time_us": 16.607, + "pct_cuda_time": 0.2280176537591676, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.607, + "pct_cuda_time": 0.2280176537591676, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.278, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04173984870403261, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04173984870403261, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 467.681, + "cuda_time_us": 140.637, + "pct_cuda_time": 1.930976020456919, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 154.107, + "cuda_time_us": 84.191, + "pct_cuda_time": 1.155960395474082, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.191, + "pct_cuda_time": 1.155960395474082, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.425, + "cuda_time_us": 8.991, + "pct_cuda_time": 0.12344834858485434, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.991, + "pct_cuda_time": 0.12344834858485434, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.552, + "cuda_time_us": 47.455, + "pct_cuda_time": 0.6515672763979827, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.799, + "pct_cuda_time": 0.6150998296355121, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.656, + "pct_cuda_time": 0.0364674467624706, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2623.494, + "cuda_time_us": 210.971, + "pct_cuda_time": 2.8966768489929158, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.507, + "cuda_time_us": 3.071, + "pct_cuda_time": 0.042165485319106626, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.071, + "pct_cuda_time": 0.042165485319106626, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1918.15, + "cuda_time_us": 64.158, + "pct_cuda_time": 0.8809030306425408, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.956, + "cuda_time_us": 22.624, + "pct_cuda_time": 0.3106323477236953, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.624, + "pct_cuda_time": 0.3106323477236953, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 522.271, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05052718527330264, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05052718527330264, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 802.382, + "cuda_time_us": 21.247, + "pct_cuda_time": 0.2917258438863753, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.592, + "pct_cuda_time": 0.035588713105543596, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.343, + "pct_cuda_time": 0.23812309081382815, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 248.114, + "cuda_time_us": 16.607, + "pct_cuda_time": 0.2280176537591676, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.607, + "pct_cuda_time": 0.2280176537591676, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.522, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04173984870403261, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04173984870403261, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 466.052, + "cuda_time_us": 140.702, + "pct_cuda_time": 1.9318684843272358, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 155.536, + "cuda_time_us": 84.991, + "pct_cuda_time": 1.1669445661856697, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.991, + "pct_cuda_time": 1.1669445661856697, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.654, + "cuda_time_us": 9.248, + "pct_cuda_time": 0.12697701342595183, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.248, + "pct_cuda_time": 0.12697701342595183, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 147.796, + "cuda_time_us": 46.463, + "pct_cuda_time": 0.6379469047156142, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.063, + "pct_cuda_time": 0.6049943925808516, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.4, + "pct_cuda_time": 0.032952512134762586, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2527.799, + "cuda_time_us": 209.821, + "pct_cuda_time": 2.8808871035950085, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.613, + "cuda_time_us": 3.167, + "pct_cuda_time": 0.04348358580449713, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.167, + "pct_cuda_time": 0.04348358580449713, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1847.564, + "cuda_time_us": 63.169, + "pct_cuda_time": 0.8673238496003407, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.972, + "cuda_time_us": 21.856, + "pct_cuda_time": 0.30008754384057135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.856, + "pct_cuda_time": 0.30008754384057135, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 532.785, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.050966552101766135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.050966552101766135, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 813.645, + "cuda_time_us": 21.441, + "pct_cuda_time": 0.29438950528393526, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.656, + "pct_cuda_time": 0.0364674467624706, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.408, + "pct_cuda_time": 0.23901555468414465, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.377, + "pct_cuda_time": 0.018906503837320034, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 177.265, + "cuda_time_us": 16.16, + "pct_cuda_time": 0.22188024837406808, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.16, + "pct_cuda_time": 0.22188024837406808, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.023, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04173984870403261, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04173984870403261, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 450.699, + "cuda_time_us": 140.445, + "pct_cuda_time": 1.928339819486138, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 148.279, + "cuda_time_us": 84.606, + "pct_cuda_time": 1.1616584340307181, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.606, + "pct_cuda_time": 1.1616584340307181, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.019, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.12434081245517083, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.12434081245517083, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.617, + "cuda_time_us": 46.783, + "pct_cuda_time": 0.6423405730002493, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.128, + "pct_cuda_time": 0.605886856451168, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.655, + "pct_cuda_time": 0.03645371654908111, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2542.817, + "cuda_time_us": 209.948, + "pct_cuda_time": 2.882630840695473, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.354, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.043057949189423114, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.043057949189423114, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1850.294, + "cuda_time_us": 64.191, + "pct_cuda_time": 0.881356127684394, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 167.788, + "cuda_time_us": 22.719, + "pct_cuda_time": 0.31193671799569633, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.719, + "pct_cuda_time": 0.31193671799569633, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 536.381, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05008781844483913, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05008781844483913, + "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 795.382, + "cuda_time_us": 21.152, + "pct_cuda_time": 0.29042147361437426, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.03427061262015309, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.344, + "pct_cuda_time": 0.23813682102721764, + "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01801403996700355, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 174.515, + "cuda_time_us": 16.672, + "pct_cuda_time": 0.2289101176294841, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.672, + "pct_cuda_time": 0.2289101176294841, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.165, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04437604967481362, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04437604967481362, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 457.383, + "cuda_time_us": 139.389, + "pct_cuda_time": 1.9138407141468428, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 148.167, + "cuda_time_us": 84.447, + "pct_cuda_time": 1.1594753301017902, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.447, + "pct_cuda_time": 1.1594753301017902, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.188, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.12346207879824385, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.12346207879824385, + "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 147.471, + "cuda_time_us": 45.95, + "pct_cuda_time": 0.6309033052468087, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.551, + "pct_cuda_time": 0.5979645233254356, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.399, + "pct_cuda_time": 0.0329387819213731, + "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.784, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04261858236095961, + "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 636.408, + "cuda_time_us": 389.915, + "pct_cuda_time": 5.3536161537608145, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04437604967481362, + "trace": "index_select(bfloat16[20, 4096], 0, int64[20])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.010105437054660526, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[20, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 385.947, + "pct_cuda_time": 5.29913466703134, + "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[20, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 4924.103, + "cuda_time_us": 138.846, + "pct_cuda_time": 1.9063852082763528, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.010091706841271041, + "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.704, + "pct_cuda_time": 0.009666070226197025, + "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.010544803883124027, + "trace": "copy_(int32[20], int32[20], True) <- _to_copy(int32[20], 3, 0, None, None, True, None) <- to(int32[20], 3, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.01098417071158753, + "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.010544803883124027, + "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.01098417071158753, + "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.010544803883124027, + "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 9.28, + "pct_cuda_time": 0.12741638025441535, + "trace": "copy_(float32[20, 128256], bfloat16[20, 128256], False) <- _to_copy(bfloat16[20, 128256], 6, None, None, None, False, None) <- to(bfloat16[20, 128256], 6, False, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::elementwise_kernel<128, 4, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1})", + "cpu_time_us": 0, + "cuda_time_us": 13.216, + "pct_cuda_time": 0.18145850015542597, + "trace": "div_(float32[20, 128256], bfloat16[20, 1])" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::SoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 35.392, + "pct_cuda_time": 0.4859397122806323, + "trace": "_softmax(float32[20, 128256], -1, False) <- softmax(float32[20, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::LogSoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 29.791, + "pct_cuda_time": 0.40903678708613006, + "trace": "_log_softmax(float32[20, 128256], -1, False) <- log_softmax(float32[20, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.02899821067859108, + "trace": "copy_(int64[20], int32[20], False) <- _to_copy(int32[20], 4, None, None, None, False, None) <- to(int32[20], 4, False, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::index_elementwise_kernel<128, 4, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1}>(long, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1})", + "cpu_time_us": 0, + "cuda_time_us": 12.992, + "pct_cuda_time": 0.1783829323561815, + "trace": "index(float32[20, 128256], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, long, 4> >(at::native::ReduceOp, unsigned int, long, 4>)", + "cpu_time_us": 0, + "cuda_time_us": 28.223, + "pct_cuda_time": 0.3875078124914185, + "trace": "argmax(float32[20, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 2.497, + "pct_cuda_time": 0.03428434283354257, + "trace": "copy_(int64[20], int64[20], False) <- _to_copy(int64[20], 4, 0, None, None, False, None) <- to(int64[20], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + } +} \ No newline at end of file