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int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.559, + "pct_cuda_time": 0.05313980025091808, + "trace": "_C::rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 3099.865, + "cuda_time_us": 145.052, + "pct_cuda_time": 0.729996619565884, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 471.102, + "cuda_time_us": 68.319, + "pct_cuda_time": 0.34382593174945286, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.583, + "pct_cuda_time": 0.34012189794088427, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 1005.874, + "cuda_time_us": 10.816, + "pct_cuda_time": 0.05443319249113837, + "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": 10.816, + "pct_cuda_time": 0.05443319249113837, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1076.456, + "cuda_time_us": 21.502, + "pct_cuda_time": 0.10821213988021977, + "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": 5.183, + "pct_cuda_time": 0.026084248953547537, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.008, + "pct_cuda_time": 0.07553008070515944, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 1.311, + "pct_cuda_time": 0.006597810221512796, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 294.837, + "cuda_time_us": 44.415, + "pct_cuda_time": 0.2235253554450731, + "trace": "" + }, + "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": 44.415, + "pct_cuda_time": 0.2235253554450731, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 121.791, + "cuda_time_us": 8.448, + "pct_cuda_time": 0.04251586632443944, + "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": 8.448, + "pct_cuda_time": 0.04251586632443944, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 628.502, + "cuda_time_us": 447.002, + "pct_cuda_time": 2.249606685458934, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 243.479, + "cuda_time_us": 270.525, + "pct_cuda_time": 1.36145889410736, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 269.789, + "pct_cuda_time": 1.3577548602987914, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 138.734, + "cuda_time_us": 33.951, + "pct_cuda_time": 0.1708636573841197, + "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": 33.951, + "pct_cuda_time": 0.1708636573841197, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 164.811, + "cuda_time_us": 142.526, + "pct_cuda_time": 0.7172841339674545, + "trace": "" + }, + "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": 142.526, + "pct_cuda_time": 0.7172841339674545, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2558.609, + "cuda_time_us": 604.345, + "pct_cuda_time": 3.0414596630969877, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.87, + "cuda_time_us": 8.128, + "pct_cuda_time": 0.04090541684245309, + "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": 8.128, + "pct_cuda_time": 0.04090541684245309, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1845.043, + "cuda_time_us": 146.495, + "pct_cuda_time": 0.7372587401987163, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 164.235, + "cuda_time_us": 69.15100000000001, + "pct_cuda_time": 0.3480131004026174, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 68.415, + "pct_cuda_time": 0.3443090665940488, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 535.987, + "cuda_time_us": 10.624, + "pct_cuda_time": 0.05346692280194656, + "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": 10.624, + "pct_cuda_time": 0.05346692280194656, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 795.887, + "cuda_time_us": 21.888, + "pct_cuda_time": 0.11015474456786581, + "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": 5.312, + "pct_cuda_time": 0.02673346140097328, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.104, + "pct_cuda_time": 0.07601321554975535, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.007408067617137174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 188.563, + "cuda_time_us": 44.832, + "pct_cuda_time": 0.22562397242628657, + "trace": "" + }, + "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": 44.832, + "pct_cuda_time": 0.22562397242628657, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.225, + "cuda_time_us": 8.448, + "pct_cuda_time": 0.04251586632443944, + "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": 8.448, + "pct_cuda_time": 0.04251586632443944, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 463.487, + "cuda_time_us": 441.274, + "pct_cuda_time": 2.220779639731379, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 162.811, + "cuda_time_us": 269.02, + "pct_cuda_time": 1.353884748887393, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 268.284, + "pct_cuda_time": 1.3501807150788243, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.694, + "cuda_time_us": 33.92, + "pct_cuda_time": 0.17070764509055228, + "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": 33.92, + "pct_cuda_time": 0.17070764509055228, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.556, + "cuda_time_us": 138.334, + "pct_cuda_time": 0.6961872457534334, + "trace": "" + }, + "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": 138.334, + "pct_cuda_time": 0.6961872457534334, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2647.601, + "cuda_time_us": 602.584, + "pct_cuda_time": 3.032597158291431, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.438, + "cuda_time_us": 8.224, + "pct_cuda_time": 0.041388551687049, + "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": 8.224, + "pct_cuda_time": 0.041388551687049, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1872.395, + "cuda_time_us": 145.375, + "pct_cuda_time": 0.7316221670117641, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.237, + "cuda_time_us": 67.935, + "pct_cuda_time": 0.34189339237106925, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.199, + "pct_cuda_time": 0.33818935856250065, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 507.434, + "cuda_time_us": 10.688, + "pct_cuda_time": 0.05378901269834384, + "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": 10.688, + "pct_cuda_time": 0.05378901269834384, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 800.507, + "cuda_time_us": 22.080000000000002, + "pct_cuda_time": 0.11112101425705763, + "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": 5.408, + "pct_cuda_time": 0.027216596245569186, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.232, + "pct_cuda_time": 0.0766573953425499, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.007247022668938539, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 210.182, + "cuda_time_us": 44.672, + "pct_cuda_time": 0.22481874768529334, + "trace": "" + }, + "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": 44.672, + "pct_cuda_time": 0.22481874768529334, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 91.176, + "cuda_time_us": 8.319, + "pct_cuda_time": 0.041866653877013695, + "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": 8.319, + "pct_cuda_time": 0.041866653877013695, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 506.356, + "cuda_time_us": 440.66599999999994, + "pct_cuda_time": 2.2177197857156044, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 175.237, + "cuda_time_us": 269.085, + "pct_cuda_time": 1.3542118714384215, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0038650787567672215, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 268.317, + "pct_cuda_time": 1.3503467926816544, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 105.304, + "cuda_time_us": 33.503, + "pct_cuda_time": 0.16860902810933884, + "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": 33.503, + "pct_cuda_time": 0.16860902810933884, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 167.847, + "cuda_time_us": 138.078, + "pct_cuda_time": 0.6948988861678442, + "trace": "" + }, + "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": 138.078, + "pct_cuda_time": 0.6948988861678442, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2514.525, + "cuda_time_us": 603.45, + "pct_cuda_time": 3.036955437202057, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.141, + "cuda_time_us": 8.416, + "pct_cuda_time": 0.0423548213762408, + "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": 8.416, + "pct_cuda_time": 0.0423548213762408, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1812.187, + "cuda_time_us": 145.536, + "pct_cuda_time": 0.7324324244073884, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 154.689, + "cuda_time_us": 68.447, + "pct_cuda_time": 0.34447011154224744, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.711, + "pct_cuda_time": 0.3407660777336788, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 536.545, + "cuda_time_us": 10.56, + "pct_cuda_time": 0.05314483290554929, + "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": 10.56, + "pct_cuda_time": 0.05314483290554929, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 748.856, + "cuda_time_us": 21.889, + "pct_cuda_time": 0.11015977722249701, + "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": 5.408, + "pct_cuda_time": 0.027216596245569186, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.072, + "pct_cuda_time": 0.07585217060155672, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 1.409, + "pct_cuda_time": 0.0070910103753711136, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 195.267, + "cuda_time_us": 44.64, + "pct_cuda_time": 0.22465770273709476, + "trace": "" + }, + "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": 44.64, + "pct_cuda_time": 0.22465770273709476, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.858, + "cuda_time_us": 8.128, + "pct_cuda_time": 0.04090541684245309, + "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": 8.128, + "pct_cuda_time": 0.04090541684245309, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 470.682, + "cuda_time_us": 441.37, + "pct_cuda_time": 2.2212627745759748, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 177.263, + "cuda_time_us": 269.757, + "pct_cuda_time": 1.3575938153505929, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 269.021, + "pct_cuda_time": 1.3538897815420246, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.634, + "cuda_time_us": 33.727, + "pct_cuda_time": 0.16973634274672925, + "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": 33.727, + "pct_cuda_time": 0.16973634274672925, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 147.14, + "cuda_time_us": 137.886, + "pct_cuda_time": 0.6939326164786525, + "trace": "" + }, + "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": 137.886, + "pct_cuda_time": 0.6939326164786525, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2376.593, + "cuda_time_us": 605.304, + "pct_cuda_time": 3.046285978888315, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.445, + "cuda_time_us": 8.16, + "pct_cuda_time": 0.04106646179065173, + "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": 8.16, + "pct_cuda_time": 0.04106646179065173, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1689.821, + "cuda_time_us": 146.78199999999998, + "pct_cuda_time": 0.7387031120778726, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.036, + "cuda_time_us": 69.375, + "pct_cuda_time": 0.3491404150400078, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 68.639, + "pct_cuda_time": 0.34543638123143916, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 502.77, + "cuda_time_us": 10.848, + "pct_cuda_time": 0.054594237439337, + "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": 10.848, + "pct_cuda_time": 0.054594237439337, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 710.893, + "cuda_time_us": 21.568, + "pct_cuda_time": 0.10854429508587947, + "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": 5.28, + "pct_cuda_time": 0.026572416452774646, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.008, + "pct_cuda_time": 0.07553008070515944, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.006441797927945369, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 178.69, + "cuda_time_us": 44.991, + "pct_cuda_time": 0.2264241645126485, + "trace": "" + }, + "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": 44.991, + "pct_cuda_time": 0.2264241645126485, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 93.199, + "cuda_time_us": 8.288, + "pct_cuda_time": 0.04171064158344626, + "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": 8.288, + "pct_cuda_time": 0.04171064158344626, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 453.427, + "cuda_time_us": 442.074, + "pct_cuda_time": 2.2248057634363447, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 162.94, + "cuda_time_us": 270.524, + "pct_cuda_time": 1.361453861452729, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 269.788, + "pct_cuda_time": 1.3577498276441604, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.59, + "cuda_time_us": 33.408, + "pct_cuda_time": 0.16813092591937412, + "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": 33.408, + "pct_cuda_time": 0.16813092591937412, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.195, + "cuda_time_us": 138.142, + "pct_cuda_time": 0.6952209760642415, + "trace": "" + }, + "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": 138.142, + "pct_cuda_time": 0.6952209760642415, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2754.525, + "cuda_time_us": 606.232, + "pct_cuda_time": 3.0509562823860756, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.322, + "cuda_time_us": 8.416, + "pct_cuda_time": 0.0423548213762408, + "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": 8.416, + "pct_cuda_time": 0.0423548213762408, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2034.263, + "cuda_time_us": 145.375, + "pct_cuda_time": 0.7316221670117641, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 154.694, + "cuda_time_us": 68.415, + "pct_cuda_time": 0.3443090665940488, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.679, + "pct_cuda_time": 0.34060503278548016, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 728.387, + "cuda_time_us": 10.496, + "pct_cuda_time": 0.052822743009152025, + "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": 10.496, + "pct_cuda_time": 0.052822743009152025, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 784.681, + "cuda_time_us": 21.824, + "pct_cuda_time": 0.10983265467146854, + "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": 5.376, + "pct_cuda_time": 0.027055551297370552, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 14.944, + "pct_cuda_time": 0.07520799080876218, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.007569112565335809, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 191.367, + "cuda_time_us": 44.64, + "pct_cuda_time": 0.22465770273709476, + "trace": "" + }, + "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": 44.64, + "pct_cuda_time": 0.22465770273709476, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.983, + "cuda_time_us": 8.159, + "pct_cuda_time": 0.041061429136020525, + "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": 8.159, + "pct_cuda_time": 0.041061429136020525, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.813, + "cuda_time_us": 444.28200000000004, + "pct_cuda_time": 2.2359178648620506, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 180.166, + "cuda_time_us": 272.06, + "pct_cuda_time": 1.3691840189662634, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 271.324, + "pct_cuda_time": 1.3654799851576949, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.872, + "cuda_time_us": 33.92, + "pct_cuda_time": 0.17070764509055228, + "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": 33.92, + "pct_cuda_time": 0.17070764509055228, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.268, + "cuda_time_us": 138.302, + "pct_cuda_time": 0.6960262008052347, + "trace": "" + }, + "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": 138.302, + "pct_cuda_time": 0.6960262008052347, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2372.49, + "cuda_time_us": 604.823, + "pct_cuda_time": 3.0438652720107044, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.542, + "cuda_time_us": 8.256, + "pct_cuda_time": 0.04154959663524763, + "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": 8.256, + "pct_cuda_time": 0.04154959663524763, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1700.088, + "cuda_time_us": 146.07800000000003, + "pct_cuda_time": 0.7351601232175029, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.744, + "cuda_time_us": 69.43900000000001, + "pct_cuda_time": 0.34946250493640507, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 68.703, + "pct_cuda_time": 0.3457584711278365, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 488.011, + "cuda_time_us": 10.592, + "pct_cuda_time": 0.05330587785374793, + "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": 10.592, + "pct_cuda_time": 0.05330587785374793, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 739.384, + "cuda_time_us": 21.728, + "pct_cuda_time": 0.10934951982687265, + "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": 5.184, + "pct_cuda_time": 0.026089281608178746, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.04, + "pct_cuda_time": 0.07569112565335809, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.007569112565335809, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 175.288, + "cuda_time_us": 44.319, + "pct_cuda_time": 0.2230422206004772, + "trace": "" + }, + "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": 44.319, + "pct_cuda_time": 0.2230422206004772, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.577, + "cuda_time_us": 8.223, + "pct_cuda_time": 0.04138351903241779, + "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": 8.223, + "pct_cuda_time": 0.04138351903241779, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 461.631, + "cuda_time_us": 442.26599999999996, + "pct_cuda_time": 2.225772033125536, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 152.93, + "cuda_time_us": 269.88399999999996, + "pct_cuda_time": 1.3582329624887561, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0038650787567672215, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 269.116, + "pct_cuda_time": 1.3543678837319888, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.689, + "cuda_time_us": 33.952, + "pct_cuda_time": 0.1708686900387509, + "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": 33.952, + "pct_cuda_time": 0.1708686900387509, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 160.791, + "cuda_time_us": 138.43, + "pct_cuda_time": 0.6966703805980292, + "trace": "" + }, + "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": 138.43, + "pct_cuda_time": 0.6966703805980292, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2406.608, + "cuda_time_us": 604.216, + "pct_cuda_time": 3.0408104506495617, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 109.515, + "cuda_time_us": 8.544, + "pct_cuda_time": 0.042999001169035336, + "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": 8.544, + "pct_cuda_time": 0.042999001169035336, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1677.156, + "cuda_time_us": 145.663, + "pct_cuda_time": 0.7330715715455518, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 149.086, + "cuda_time_us": 68.574, + "pct_cuda_time": 0.3451092586804107, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0036990011539373797, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.839, + "pct_cuda_time": 0.34141025752647336, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 479.332, + "cuda_time_us": 10.528, + "pct_cuda_time": 0.05298378795735066, + "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": 10.528, + "pct_cuda_time": 0.05298378795735066, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 722.888, + "cuda_time_us": 21.857, + "pct_cuda_time": 0.10999873227429838, + "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": 5.408, + "pct_cuda_time": 0.027216596245569186, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.104, + "pct_cuda_time": 0.07601321554975535, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 1.345, + "pct_cuda_time": 0.0067689204789738435, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 181.99, + "cuda_time_us": 44.704, + "pct_cuda_time": 0.22497979263349202, + "trace": "" + }, + "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": 44.704, + "pct_cuda_time": 0.22497979263349202, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.67, + "cuda_time_us": 8.256, + "pct_cuda_time": 0.04154959663524763, + "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": 8.256, + "pct_cuda_time": 0.04154959663524763, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 465.294, + "cuda_time_us": 441.753, + "pct_cuda_time": 2.223190281299727, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 169.317, + "cuda_time_us": 269.628, + "pct_cuda_time": 1.356944602903167, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0036990011539373797, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 268.893, + "pct_cuda_time": 1.3532456017492296, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.805, + "cuda_time_us": 34.111, + "pct_cuda_time": 0.17166888212511286, + "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": 34.111, + "pct_cuda_time": 0.17166888212511286, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.03, + "cuda_time_us": 138.014, + "pct_cuda_time": 0.694576796271447, + "trace": "" + }, + "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": 138.014, + "pct_cuda_time": 0.694576796271447, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2535.457, + "cuda_time_us": 608.664, + "pct_cuda_time": 3.0631956984491717, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.437, + "cuda_time_us": 8.192, + "pct_cuda_time": 0.04122750673885036, + "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": 8.192, + "pct_cuda_time": 0.04122750673885036, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1784.31, + "cuda_time_us": 144.958, + "pct_cuda_time": 0.7295235500305506, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 180.869, + "cuda_time_us": 68.447, + "pct_cuda_time": 0.34447011154224744, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.711, + "pct_cuda_time": 0.3407660777336788, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 516.218, + "cuda_time_us": 10.624, + "pct_cuda_time": 0.05346692280194656, + "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": 10.624, + "pct_cuda_time": 0.05346692280194656, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 743.361, + "cuda_time_us": 21.44, + "pct_cuda_time": 0.10790011529308494, + "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": 5.088, + "pct_cuda_time": 0.025606146763582843, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.072, + "pct_cuda_time": 0.07585217060155672, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.006441797927945369, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 183.483, + "cuda_time_us": 44.447, + "pct_cuda_time": 0.22368640039327173, + "trace": "" + }, + "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": 44.447, + "pct_cuda_time": 0.22368640039327173, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.434, + "cuda_time_us": 8.319, + "pct_cuda_time": 0.041866653877013695, + "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": 8.319, + "pct_cuda_time": 0.041866653877013695, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 527.601, + "cuda_time_us": 447.19499999999994, + "pct_cuda_time": 2.250577987802757, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.883, + "cuda_time_us": 270.52399999999994, + "pct_cuda_time": 1.3614538614527285, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0038650787567672215, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 269.756, + "pct_cuda_time": 1.3575887826959616, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 110.731, + "cuda_time_us": 34.304, + "pct_cuda_time": 0.17264018446893592, + "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": 34.304, + "pct_cuda_time": 0.17264018446893592, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 203.146, + "cuda_time_us": 142.367, + "pct_cuda_time": 0.7164839418810924, + "trace": "" + }, + "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": 142.367, + "pct_cuda_time": 0.7164839418810924, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2376.277, + "cuda_time_us": 604.537, + "pct_cuda_time": 3.0424259327861796, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.074, + "cuda_time_us": 8.128, + "pct_cuda_time": 0.04090541684245309, + "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": 8.128, + "pct_cuda_time": 0.04090541684245309, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1713.086, + "cuda_time_us": 146.27100000000002, + "pct_cuda_time": 0.7361314255613258, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 147.533, + "cuda_time_us": 68.63900000000001, + "pct_cuda_time": 0.34543638123143927, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.903, + "pct_cuda_time": 0.3417323474228707, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 487.292, + "cuda_time_us": 10.56, + "pct_cuda_time": 0.05314483290554929, + "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": 10.56, + "pct_cuda_time": 0.05314483290554929, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 757.714, + "cuda_time_us": 21.953, + "pct_cuda_time": 0.11048186711889428, + "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": 5.441, + "pct_cuda_time": 0.027382673848399024, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.04, + "pct_cuda_time": 0.07569112565335809, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.007408067617137174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 176.549, + "cuda_time_us": 45.119, + "pct_cuda_time": 0.22706834430544304, + "trace": "" + }, + "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": 45.119, + "pct_cuda_time": 0.22706834430544304, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.63, + "cuda_time_us": 8.192, + "pct_cuda_time": 0.04122750673885036, + "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": 8.192, + "pct_cuda_time": 0.04122750673885036, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 441.433, + "cuda_time_us": 441.946, + "pct_cuda_time": 2.22416158364355, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 153.85, + "cuda_time_us": 269.82, + "pct_cuda_time": 1.357910872592359, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0036990011539373797, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 269.085, + "pct_cuda_time": 1.3542118714384215, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.962, + "cuda_time_us": 34.24, + "pct_cuda_time": 0.17231809457253863, + "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": 34.24, + "pct_cuda_time": 0.17231809457253863, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.588, + "cuda_time_us": 137.886, + "pct_cuda_time": 0.6939326164786525, + "trace": "" + }, + "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": 137.886, + "pct_cuda_time": 0.6939326164786525, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2516.478, + "cuda_time_us": 603.959, + "pct_cuda_time": 3.0395170584093414, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.131, + "cuda_time_us": 8.32, + "pct_cuda_time": 0.0418716865316449, + "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": 8.32, + "pct_cuda_time": 0.0418716865316449, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1809.87, + "cuda_time_us": 146.526, + "pct_cuda_time": 0.7374147524922837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.509, + "cuda_time_us": 68.64, + "pct_cuda_time": 0.3454414138860704, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.904, + "pct_cuda_time": 0.3417373800775018, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 521.416, + "cuda_time_us": 10.976, + "pct_cuda_time": 0.05523841723213155, + "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": 10.976, + "pct_cuda_time": 0.05523841723213155, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 770.884, + "cuda_time_us": 22.175, + "pct_cuda_time": 0.11159911644702232, + "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": 5.28, + "pct_cuda_time": 0.026572416452774646, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.264, + "pct_cuda_time": 0.07681844029074852, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 1.631, + "pct_cuda_time": 0.008208259703499138, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 215.177, + "cuda_time_us": 44.735, + "pct_cuda_time": 0.22513580492705942, + "trace": "" + }, + "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": 44.735, + "pct_cuda_time": 0.22513580492705942, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.266, + "cuda_time_us": 8.064, + "pct_cuda_time": 0.04058332694605582, + "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": 8.064, + "pct_cuda_time": 0.04058332694605582, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 475.339, + "cuda_time_us": 441.049, + "pct_cuda_time": 2.219647292439357, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 169.848, + "cuda_time_us": 269.532, + "pct_cuda_time": 1.3564614680585712, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 268.796, + "pct_cuda_time": 1.3527574342500026, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.551, + "cuda_time_us": 33.631, + "pct_cuda_time": 0.16925320790213336, + "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": 33.631, + "pct_cuda_time": 0.16925320790213336, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.981, + "cuda_time_us": 137.886, + "pct_cuda_time": 0.6939326164786525, + "trace": "" + }, + "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": 137.886, + "pct_cuda_time": 0.6939326164786525, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2460.829, + "cuda_time_us": 605.5900000000001, + "pct_cuda_time": 3.047725318112841, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.134, + "cuda_time_us": 8.479, + "pct_cuda_time": 0.04267187861800686, + "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": 8.479, + "pct_cuda_time": 0.04267187861800686, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1757.05, + "cuda_time_us": 146.174, + "pct_cuda_time": 0.7356432580620987, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 159.1, + "cuda_time_us": 68.863, + "pct_cuda_time": 0.3465636958688296, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 68.127, + "pct_cuda_time": 0.34285966206026103, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 468.587, + "cuda_time_us": 10.752, + "pct_cuda_time": 0.054111102594741105, + "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": 10.752, + "pct_cuda_time": 0.054111102594741105, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 712.169, + "cuda_time_us": 21.727, + "pct_cuda_time": 0.10934448717224143, + "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": 5.311, + "pct_cuda_time": 0.026728428746342077, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.104, + "pct_cuda_time": 0.07601321554975535, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.006602842876144003, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 180.025, + "cuda_time_us": 44.832, + "pct_cuda_time": 0.22562397242628657, + "trace": "" + }, + "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": 44.832, + "pct_cuda_time": 0.22562397242628657, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.222, + "cuda_time_us": 8.223, + "pct_cuda_time": 0.04138351903241779, + "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": 8.223, + "pct_cuda_time": 0.04138351903241779, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 478.514, + "cuda_time_us": 442.71400000000006, + "pct_cuda_time": 2.2280266624003175, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 180.082, + "cuda_time_us": 270.908, + "pct_cuda_time": 1.3633864008311125, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 270.172, + "pct_cuda_time": 1.359682367022544, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.777, + "cuda_time_us": 33.792, + "pct_cuda_time": 0.17006346529775776, + "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": 33.792, + "pct_cuda_time": 0.17006346529775776, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.017, + "cuda_time_us": 138.014, + "pct_cuda_time": 0.694576796271447, + "trace": "" + }, + "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": 138.014, + "pct_cuda_time": 0.694576796271447, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2630.49, + "cuda_time_us": 604.952, + "pct_cuda_time": 3.0445144844581304, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.153, + "cuda_time_us": 8.32, + "pct_cuda_time": 0.0418716865316449, + "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": 8.32, + "pct_cuda_time": 0.0418716865316449, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1931.061, + "cuda_time_us": 146.238, + "pct_cuda_time": 0.735965347958496, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 153.901, + "cuda_time_us": 69.087, + "pct_cuda_time": 0.3476910105062201, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 68.351, + "pct_cuda_time": 0.3439869766976515, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 469.707, + "cuda_time_us": 10.848, + "pct_cuda_time": 0.054594237439337, + "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": 10.848, + "pct_cuda_time": 0.054594237439337, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 963.053, + "cuda_time_us": 21.6, + "pct_cuda_time": 0.10870534003407811, + "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": 5.12, + "pct_cuda_time": 0.025767191711781476, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.168, + "pct_cuda_time": 0.07633530544615262, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.006602842876144003, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 194.564, + "cuda_time_us": 44.703, + "pct_cuda_time": 0.22497475997886085, + "trace": "" + }, + "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": 44.703, + "pct_cuda_time": 0.22497475997886085, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.221, + "cuda_time_us": 8.288, + "pct_cuda_time": 0.04171064158344626, + "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": 8.288, + "pct_cuda_time": 0.04171064158344626, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 468.354, + "cuda_time_us": 442.106, + "pct_cuda_time": 2.224966808384543, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 169.748, + "cuda_time_us": 269.725, + "pct_cuda_time": 1.3574327704023943, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.003709066463199794, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 268.988, + "pct_cuda_time": 1.3537237039391945, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.605, + "cuda_time_us": 34.111, + "pct_cuda_time": 0.17166888212511286, + "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": 34.111, + "pct_cuda_time": 0.17166888212511286, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.324, + "cuda_time_us": 138.27, + "pct_cuda_time": 0.6958651558570361, + "trace": "" + }, + "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": 138.27, + "pct_cuda_time": 0.6958651558570361, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2465.988, + "cuda_time_us": 605.178, + "pct_cuda_time": 3.045651864404783, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.731, + "cuda_time_us": 8.319, + "pct_cuda_time": 0.041866653877013695, + "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": 8.319, + "pct_cuda_time": 0.041866653877013695, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1803.448, + "cuda_time_us": 145.50400000000002, + "pct_cuda_time": 0.7322713794591899, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 161.672, + "cuda_time_us": 68.32, + "pct_cuda_time": 0.343830964404084, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.003709066463199794, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.583, + "pct_cuda_time": 0.34012189794088427, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 557.335, + "cuda_time_us": 10.432, + "pct_cuda_time": 0.05250065311275476, + "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": 10.432, + "pct_cuda_time": 0.05250065311275476, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 748.725, + "cuda_time_us": 22.112000000000002, + "pct_cuda_time": 0.11128205920525626, + "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": 5.503, + "pct_cuda_time": 0.02769469843553388, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.041, + "pct_cuda_time": 0.0756961583079893, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 1.568, + "pct_cuda_time": 0.007891202461733077, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 182.102, + "cuda_time_us": 44.64, + "pct_cuda_time": 0.22465770273709476, + "trace": "" + }, + "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": 44.64, + "pct_cuda_time": 0.22465770273709476, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.952, + "cuda_time_us": 8.128, + "pct_cuda_time": 0.04090541684245309, + "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": 8.128, + "pct_cuda_time": 0.04090541684245309, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 446.464, + "cuda_time_us": 443.227, + "pct_cuda_time": 2.2306084142261264, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 156.567, + "cuda_time_us": 270.877, + "pct_cuda_time": 1.3632303885375452, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 270.141, + "pct_cuda_time": 1.3595263547289766, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.162, + "cuda_time_us": 34.176, + "pct_cuda_time": 0.17199600467614135, + "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": 34.176, + "pct_cuda_time": 0.17199600467614135, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.263, + "cuda_time_us": 138.174, + "pct_cuda_time": 0.6953820210124402, + "trace": "" + }, + "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": 138.174, + "pct_cuda_time": 0.6953820210124402, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2395.617, + "cuda_time_us": 605.495, + "pct_cuda_time": 3.047247215922876, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.976, + "cuda_time_us": 8.287, + "pct_cuda_time": 0.041705608928815065, + "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": 8.287, + "pct_cuda_time": 0.041705608928815065, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1689.493, + "cuda_time_us": 147.453, + "pct_cuda_time": 0.7420800233354129, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 157.084, + "cuda_time_us": 68.894, + "pct_cuda_time": 0.3467197081623971, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0036990011539373797, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 68.159, + "pct_cuda_time": 0.3430207070084597, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 501.993, + "cuda_time_us": 11.296, + "pct_cuda_time": 0.05684886671411788, + "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": 11.296, + "pct_cuda_time": 0.05684886671411788, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 711.12, + "cuda_time_us": 22.048000000000002, + "pct_cuda_time": 0.110959969308859, + "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": 5.248, + "pct_cuda_time": 0.026411371504576012, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.136, + "pct_cuda_time": 0.07617426049795398, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 1.664, + "pct_cuda_time": 0.008374337306328979, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 177.173, + "cuda_time_us": 45.215, + "pct_cuda_time": 0.22755147915003898, + "trace": "" + }, + "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": 45.215, + "pct_cuda_time": 0.22755147915003898, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.198, + "cuda_time_us": 8.256, + "pct_cuda_time": 0.04154959663524763, + "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": 8.256, + "pct_cuda_time": 0.04154959663524763, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 483.329, + "cuda_time_us": 441.49899999999997, + "pct_cuda_time": 2.2219119870234003, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 169.328, + "cuda_time_us": 269.405, + "pct_cuda_time": 1.3558223209204077, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0038650787567672215, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 268.637, + "pct_cuda_time": 1.3519572421636408, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.939, + "cuda_time_us": 33.791, + "pct_cuda_time": 0.17005843264312653, + "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": 33.791, + "pct_cuda_time": 0.17005843264312653, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 162.007, + "cuda_time_us": 138.303, + "pct_cuda_time": 0.696031233459866, + "trace": "" + }, + "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": 138.303, + "pct_cuda_time": 0.696031233459866, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2431.163, + "cuda_time_us": 602.872, + "pct_cuda_time": 3.034046562825219, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.24, + "cuda_time_us": 8.511, + "pct_cuda_time": 0.042832923566205494, + "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": 8.511, + "pct_cuda_time": 0.042832923566205494, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1762.434, + "cuda_time_us": 145.02300000000002, + "pct_cuda_time": 0.7298506725815792, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 160.456, + "cuda_time_us": 68.287, + "pct_cuda_time": 0.34366488680125423, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.551, + "pct_cuda_time": 0.33996085299268564, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 523.07, + "cuda_time_us": 10.561, + "pct_cuda_time": 0.0531498655601805, + "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": 10.561, + "pct_cuda_time": 0.0531498655601805, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 723.731, + "cuda_time_us": 21.568, + "pct_cuda_time": 0.10854429508587947, + "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": 5.184, + "pct_cuda_time": 0.026089281608178746, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.104, + "pct_cuda_time": 0.07601321554975535, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.006441797927945369, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 181.666, + "cuda_time_us": 44.607, + "pct_cuda_time": 0.2244916251342649, + "trace": "" + }, + "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": 44.607, + "pct_cuda_time": 0.2244916251342649, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.191, + "cuda_time_us": 8.223, + "pct_cuda_time": 0.04138351903241779, + "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": 8.223, + "pct_cuda_time": 0.04138351903241779, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 448.008, + "cuda_time_us": 441.11499999999995, + "pct_cuda_time": 2.2199794476450165, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 161.911, + "cuda_time_us": 269.30899999999997, + "pct_cuda_time": 1.355339186075812, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0038650787567672215, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 268.541, + "pct_cuda_time": 1.3514741073190446, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.133, + "cuda_time_us": 33.984, + "pct_cuda_time": 0.17102973498694954, + "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": 33.984, + "pct_cuda_time": 0.17102973498694954, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.06, + "cuda_time_us": 137.822, + "pct_cuda_time": 0.6936105265822552, + "trace": "" + }, + "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": 137.822, + "pct_cuda_time": 0.6936105265822552, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2438.707, + "cuda_time_us": 602.9670000000001, + "pct_cuda_time": 3.034524665015184, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.394, + "cuda_time_us": 8.512, + "pct_cuda_time": 0.042837956220836707, + "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": 8.512, + "pct_cuda_time": 0.042837956220836707, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1769.558, + "cuda_time_us": 145.43800000000002, + "pct_cuda_time": 0.7319392242535302, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 149.082, + "cuda_time_us": 68.799, + "pct_cuda_time": 0.3462416059724324, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 68.063, + "pct_cuda_time": 0.3425375721638638, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 516.61, + "cuda_time_us": 10.432, + "pct_cuda_time": 0.05250065311275476, + "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": 10.432, + "pct_cuda_time": 0.05250065311275476, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 784.68, + "cuda_time_us": 21.568, + "pct_cuda_time": 0.10854429508587947, + "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": 5.344, + "pct_cuda_time": 0.02689450634917192, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 14.944, + "pct_cuda_time": 0.07520799080876218, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.006441797927945369, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 176.675, + "cuda_time_us": 44.639, + "pct_cuda_time": 0.22465267008246353, + "trace": "" + }, + "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": 44.639, + "pct_cuda_time": 0.22465267008246353, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.482, + "cuda_time_us": 8.256, + "pct_cuda_time": 0.04154959663524763, + "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": 8.256, + "pct_cuda_time": 0.04154959663524763, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 447.713, + "cuda_time_us": 440.761, + "pct_cuda_time": 2.2181978879055695, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 155.564, + "cuda_time_us": 269.019, + "pct_cuda_time": 1.3538797162327618, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0036990011539373797, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 268.284, + "pct_cuda_time": 1.3501807150788243, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.254, + "cuda_time_us": 34.144, + "pct_cuda_time": 0.17183495972794272, + "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": 34.144, + "pct_cuda_time": 0.17183495972794272, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.103, + "cuda_time_us": 137.598, + "pct_cuda_time": 0.6924832119448648, + "trace": "" + }, + "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": 137.598, + "pct_cuda_time": 0.6924832119448648, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2328.873, + "cuda_time_us": 604.921, + "pct_cuda_time": 3.0443584721645633, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.921, + "cuda_time_us": 8.416, + "pct_cuda_time": 0.0423548213762408, + "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": 8.416, + "pct_cuda_time": 0.0423548213762408, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1642.292, + "cuda_time_us": 145.791, + "pct_cuda_time": 0.7337157513383463, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.132, + "cuda_time_us": 68.703, + "pct_cuda_time": 0.3457584711278365, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.967, + "pct_cuda_time": 0.3420544373192679, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 466.443, + "cuda_time_us": 10.784, + "pct_cuda_time": 0.054272147542939735, + "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": 10.784, + "pct_cuda_time": 0.054272147542939735, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 702.598, + "cuda_time_us": 21.76, + "pct_cuda_time": 0.10951056477507128, + "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": 5.024, + "pct_cuda_time": 0.025284056867185576, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.232, + "pct_cuda_time": 0.0766573953425499, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.007569112565335809, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 179.289, + "cuda_time_us": 44.544, + "pct_cuda_time": 0.22417456789249882, + "trace": "" + }, + "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": 44.544, + "pct_cuda_time": 0.22417456789249882, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.163, + "cuda_time_us": 8.192, + "pct_cuda_time": 0.04122750673885036, + "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": 8.192, + "pct_cuda_time": 0.04122750673885036, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 456.075, + "cuda_time_us": 442.522, + "pct_cuda_time": 2.227060392711125, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.875, + "cuda_time_us": 270.332, + "pct_cuda_time": 1.360487591763537, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 269.596, + "pct_cuda_time": 1.3567835579549685, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.153, + "cuda_time_us": 33.664, + "pct_cuda_time": 0.1694192855049632, + "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": 33.664, + "pct_cuda_time": 0.1694192855049632, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.228, + "cuda_time_us": 138.526, + "pct_cuda_time": 0.6971535154426253, + "trace": "" + }, + "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": 138.526, + "pct_cuda_time": 0.6971535154426253, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2453.531, + "cuda_time_us": 602.839, + "pct_cuda_time": 3.0338804852223897, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.25, + "cuda_time_us": 8.096, + "pct_cuda_time": 0.040744371894254464, + "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": 8.096, + "pct_cuda_time": 0.040744371894254464, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1767.45, + "cuda_time_us": 145.534, + "pct_cuda_time": 0.732422359098126, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 176.923, + "cuda_time_us": 68.416, + "pct_cuda_time": 0.34431409924867995, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0038650787567672215, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.648, + "pct_cuda_time": 0.3404490204919127, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 548.726, + "cuda_time_us": 10.624, + "pct_cuda_time": 0.05346692280194656, + "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": 10.624, + "pct_cuda_time": 0.05346692280194656, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 715.21, + "cuda_time_us": 21.791, + "pct_cuda_time": 0.10966657706863871, + "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": 5.28, + "pct_cuda_time": 0.026572416452774646, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.039, + "pct_cuda_time": 0.07568609299872688, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.007408067617137174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 176.139, + "cuda_time_us": 44.703, + "pct_cuda_time": 0.22497475997886085, + "trace": "" + }, + "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": 44.703, + "pct_cuda_time": 0.22497475997886085, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.197, + "cuda_time_us": 8.287, + "pct_cuda_time": 0.041705608928815065, + "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": 8.287, + "pct_cuda_time": 0.041705608928815065, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 463.473, + "cuda_time_us": 440.922, + "pct_cuda_time": 2.219008145301194, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 174.788, + "cuda_time_us": 269.565, + "pct_cuda_time": 1.3566275456614012, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0038650787567672215, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 268.797, + "pct_cuda_time": 1.3527624669046339, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.932, + "cuda_time_us": 33.663, + "pct_cuda_time": 0.16941425285033196, + "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": 33.663, + "pct_cuda_time": 0.16941425285033196, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.214, + "cuda_time_us": 137.694, + "pct_cuda_time": 0.6929663467894606, + "trace": "" + }, + "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": 137.694, + "pct_cuda_time": 0.6929663467894606, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2624.326, + "cuda_time_us": 604.504, + "pct_cuda_time": 3.0422598551833495, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.425, + "cuda_time_us": 8.576, + "pct_cuda_time": 0.04316004611723398, + "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": 8.576, + "pct_cuda_time": 0.04316004611723398, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1938.872, + "cuda_time_us": 144.893, + "pct_cuda_time": 0.7291964274795222, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.204, + "cuda_time_us": 68.223, + "pct_cuda_time": 0.3433427969048569, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.487, + "pct_cuda_time": 0.3396387630962883, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 475.21, + "cuda_time_us": 10.527, + "pct_cuda_time": 0.05297875530271944, + "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": 10.527, + "pct_cuda_time": 0.05297875530271944, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 972.729, + "cuda_time_us": 21.536, + "pct_cuda_time": 0.10838325013768084, + "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": 5.28, + "pct_cuda_time": 0.026572416452774646, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 14.976, + "pct_cuda_time": 0.07536903575696083, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.006441797927945369, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 195.151, + "cuda_time_us": 44.607, + "pct_cuda_time": 0.2244916251342649, + "trace": "" + }, + "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": 44.607, + "pct_cuda_time": 0.2244916251342649, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 92.238, + "cuda_time_us": 8.256, + "pct_cuda_time": 0.04154959663524763, + "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": 8.256, + "pct_cuda_time": 0.04154959663524763, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 450.452, + "cuda_time_us": 442.779, + "pct_cuda_time": 2.228353784951346, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.055, + "cuda_time_us": 269.726, + "pct_cuda_time": 1.3574378030570255, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.705, + "pct_cuda_time": 0.0035480215150011604, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 269.021, + "pct_cuda_time": 1.3538897815420246, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.403, + "cuda_time_us": 34.175, + "pct_cuda_time": 0.17199097202151012, + "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": 34.175, + "pct_cuda_time": 0.17199097202151012, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 135.85, + "cuda_time_us": 138.878, + "pct_cuda_time": 0.69892500987281, + "trace": "" + }, + "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": 138.878, + "pct_cuda_time": 0.69892500987281, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2393.463, + "cuda_time_us": 604.92, + "pct_cuda_time": 3.0443534395099316, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.033, + "cuda_time_us": 8.255, + "pct_cuda_time": 0.04154456398061643, + "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": 8.255, + "pct_cuda_time": 0.04154456398061643, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1713.1, + "cuda_time_us": 144.287, + "pct_cuda_time": 0.7261466387730106, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.06, + "cuda_time_us": 67.87100000000001, + "pct_cuda_time": 0.34157130247467204, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.135, + "pct_cuda_time": 0.3378672686661034, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 482.703, + "cuda_time_us": 10.368, + "pct_cuda_time": 0.05217856321635749, + "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": 10.368, + "pct_cuda_time": 0.05217856321635749, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 726.406, + "cuda_time_us": 21.408, + "pct_cuda_time": 0.1077390703448863, + "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": 5.12, + "pct_cuda_time": 0.025767191711781476, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.008, + "pct_cuda_time": 0.07553008070515944, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.006441797927945369, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 197.07, + "cuda_time_us": 44.64, + "pct_cuda_time": 0.22465770273709476, + "trace": "" + }, + "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": 44.64, + "pct_cuda_time": 0.22465770273709476, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.067, + "cuda_time_us": 8.224, + "pct_cuda_time": 0.041388551687049, + "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": 8.224, + "pct_cuda_time": 0.041388551687049, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 458.379, + "cuda_time_us": 444.154, + "pct_cuda_time": 2.235273685069256, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.968, + "cuda_time_us": 271.932, + "pct_cuda_time": 1.3685398391734689, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0036990011539373797, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 271.197, + "pct_cuda_time": 1.3648408380195316, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.577, + "cuda_time_us": 33.824, + "pct_cuda_time": 0.17022451024595636, + "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": 33.824, + "pct_cuda_time": 0.17022451024595636, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.928, + "cuda_time_us": 138.398, + "pct_cuda_time": 0.6965093356498306, + "trace": "" + }, + "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": 138.398, + "pct_cuda_time": 0.6965093356498306, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2419.005, + "cuda_time_us": 604.44, + "pct_cuda_time": 3.0419377652869524, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.988, + "cuda_time_us": 8.48, + "pct_cuda_time": 0.04267691127263807, + "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": 8.48, + "pct_cuda_time": 0.04267691127263807, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1717.3, + "cuda_time_us": 146.079, + "pct_cuda_time": 0.735165155872134, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 169.992, + "cuda_time_us": 68.51100000000001, + "pct_cuda_time": 0.34479220143864475, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.775, + "pct_cuda_time": 0.3410881676300761, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 497.468, + "cuda_time_us": 10.624, + "pct_cuda_time": 0.05346692280194656, + "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": 10.624, + "pct_cuda_time": 0.05346692280194656, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 725.269, + "cuda_time_us": 21.857, + "pct_cuda_time": 0.10999873227429838, + "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": 5.472, + "pct_cuda_time": 0.027538686141966452, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 14.88, + "pct_cuda_time": 0.07488590091236491, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 1.505, + "pct_cuda_time": 0.007574145219967016, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 176.91, + "cuda_time_us": 45.087, + "pct_cuda_time": 0.22690729935724444, + "trace": "" + }, + "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": 45.087, + "pct_cuda_time": 0.22690729935724444, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.386, + "cuda_time_us": 8.224, + "pct_cuda_time": 0.041388551687049, + "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": 8.224, + "pct_cuda_time": 0.041388551687049, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 478.026, + "cuda_time_us": 441.65700000000004, + "pct_cuda_time": 2.2227071464551313, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 156.134, + "cuda_time_us": 270.748, + "pct_cuda_time": 1.3625811760901192, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0038650787567672215, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 269.98, + "pct_cuda_time": 1.3587160973333523, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 123.5, + "cuda_time_us": 33.535, + "pct_cuda_time": 0.16877007305753744, + "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": 33.535, + "pct_cuda_time": 0.16877007305753744, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.879, + "cuda_time_us": 137.374, + "pct_cuda_time": 0.6913558973074743, + "trace": "" + }, + "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": 137.374, + "pct_cuda_time": 0.6913558973074743, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2304.474, + "cuda_time_us": 604.024, + "pct_cuda_time": 3.03984418096037, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.314, + "cuda_time_us": 8.064, + "pct_cuda_time": 0.04058332694605582, + "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": 8.064, + "pct_cuda_time": 0.04058332694605582, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1649.186, + "cuda_time_us": 145.43800000000002, + "pct_cuda_time": 0.7319392242535302, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.485, + "cuda_time_us": 68.063, + "pct_cuda_time": 0.3425375721638638, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.327, + "pct_cuda_time": 0.3388335383552952, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 470.659, + "cuda_time_us": 10.592, + "pct_cuda_time": 0.05330587785374793, + "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": 10.592, + "pct_cuda_time": 0.05330587785374793, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 706.545, + "cuda_time_us": 21.888, + "pct_cuda_time": 0.11015474456786581, + "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": 5.088, + "pct_cuda_time": 0.025606146763582843, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.328, + "pct_cuda_time": 0.0771405301871458, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.007408067617137174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 175.399, + "cuda_time_us": 44.895, + "pct_cuda_time": 0.22594102966805263, + "trace": "" + }, + "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": 44.895, + "pct_cuda_time": 0.22594102966805263, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.342, + "cuda_time_us": 8.224, + "pct_cuda_time": 0.041388551687049, + "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": 8.224, + "pct_cuda_time": 0.041388551687049, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 439.768, + "cuda_time_us": 442.298, + "pct_cuda_time": 2.225933078073735, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.132, + "cuda_time_us": 270.46099999999996, + "pct_cuda_time": 1.3611368042109626, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0038650787567672215, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 269.693, + "pct_cuda_time": 1.3572717254541955, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.599, + "cuda_time_us": 33.855, + "pct_cuda_time": 0.1703805225395238, + "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": 33.855, + "pct_cuda_time": 0.1703805225395238, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 133.978, + "cuda_time_us": 137.982, + "pct_cuda_time": 0.6944157513232484, + "trace": "" + }, + "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": 137.982, + "pct_cuda_time": 0.6944157513232484, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2465.223, + "cuda_time_us": 605.207, + "pct_cuda_time": 3.0457978113890882, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 98.288, + "cuda_time_us": 8.639, + "pct_cuda_time": 0.04347710335900003, + "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": 8.639, + "pct_cuda_time": 0.04347710335900003, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1768.289, + "cuda_time_us": 145.502, + "pct_cuda_time": 0.7322613141499275, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 164.296, + "cuda_time_us": 68.19099999999999, + "pct_cuda_time": 0.3431817519566583, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.704, + "pct_cuda_time": 0.0035429888603699528, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.487, + "pct_cuda_time": 0.3396387630962883, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 513.295, + "cuda_time_us": 10.752, + "pct_cuda_time": 0.054111102594741105, + "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": 10.752, + "pct_cuda_time": 0.054111102594741105, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 747.751, + "cuda_time_us": 21.407000000000004, + "pct_cuda_time": 0.10773403769025511, + "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": 5.151, + "pct_cuda_time": 0.025923204005348904, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 14.976, + "pct_cuda_time": 0.07536903575696083, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.006441797927945369, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 184.242, + "cuda_time_us": 45.152, + "pct_cuda_time": 0.2272344219082729, + "trace": "" + }, + "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": 45.152, + "pct_cuda_time": 0.2272344219082729, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.266, + "cuda_time_us": 8.321, + "pct_cuda_time": 0.041876719186276105, + "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": 8.321, + "pct_cuda_time": 0.041876719186276105, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 444.0, + "cuda_time_us": 442.745, + "pct_cuda_time": 2.2281826746938846, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 155.551, + "cuda_time_us": 270.555, + "pct_cuda_time": 1.3616098737462965, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0036990011539373797, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 269.82, + "pct_cuda_time": 1.357910872592359, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.305, + "cuda_time_us": 34.336, + "pct_cuda_time": 0.17280122941713452, + "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": 34.336, + "pct_cuda_time": 0.17280122941713452, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.849, + "cuda_time_us": 137.854, + "pct_cuda_time": 0.6937715715304539, + "trace": "" + }, + "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": 137.854, + "pct_cuda_time": 0.6937715715304539, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2460.4, + "cuda_time_us": 603.511, + "pct_cuda_time": 3.0372624291345605, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.865, + "cuda_time_us": 8.256, + "pct_cuda_time": 0.04154959663524763, + "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": 8.256, + "pct_cuda_time": 0.04154959663524763, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1780.963, + "cuda_time_us": 145.117, + "pct_cuda_time": 0.7303237421169125, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.773, + "cuda_time_us": 68.415, + "pct_cuda_time": 0.3443090665940488, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.679, + "pct_cuda_time": 0.34060503278548016, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 490.624, + "cuda_time_us": 10.496, + "pct_cuda_time": 0.052822743009152025, + "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": 10.496, + "pct_cuda_time": 0.052822743009152025, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 788.77, + "cuda_time_us": 21.407, + "pct_cuda_time": 0.10773403769025508, + "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": 5.248, + "pct_cuda_time": 0.026411371504576012, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 14.879, + "pct_cuda_time": 0.0748808682577337, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.006441797927945369, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 197.214, + "cuda_time_us": 44.799, + "pct_cuda_time": 0.22545789482345668, + "trace": "" + }, + "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": 44.799, + "pct_cuda_time": 0.22545789482345668, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.705, + "cuda_time_us": 8.192, + "pct_cuda_time": 0.04122750673885036, + "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": 8.192, + "pct_cuda_time": 0.04122750673885036, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 456.271, + "cuda_time_us": 441.9459999999999, + "pct_cuda_time": 2.2241615836435495, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.251, + "cuda_time_us": 269.59599999999995, + "pct_cuda_time": 1.3567835579549683, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0038650787567672215, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 268.828, + "pct_cuda_time": 1.3529184791982012, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.149, + "cuda_time_us": 34.592, + "pct_cuda_time": 0.1740895890027236, + "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": 34.592, + "pct_cuda_time": 0.1740895890027236, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.749, + "cuda_time_us": 137.758, + "pct_cuda_time": 0.6932884366858579, + "trace": "" + }, + "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": 137.758, + "pct_cuda_time": 0.6932884366858579, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2369.297, + "cuda_time_us": 602.967, + "pct_cuda_time": 3.0345246650151836, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.607, + "cuda_time_us": 8.128, + "pct_cuda_time": 0.04090541684245309, + "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": 8.128, + "pct_cuda_time": 0.04090541684245309, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1697.747, + "cuda_time_us": 146.04500000000002, + "pct_cuda_time": 0.7349940456146731, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 153.072, + "cuda_time_us": 68.863, + "pct_cuda_time": 0.3465636958688296, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 68.127, + "pct_cuda_time": 0.34285966206026103, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 472.792, + "cuda_time_us": 10.464, + "pct_cuda_time": 0.052661698060953395, + "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": 10.464, + "pct_cuda_time": 0.052661698060953395, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 750.684, + "cuda_time_us": 21.855, + "pct_cuda_time": 0.10998866696503597, + "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": 5.184, + "pct_cuda_time": 0.026089281608178746, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.168, + "pct_cuda_time": 0.07633530544615262, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.007564079910704601, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 174.811, + "cuda_time_us": 44.863, + "pct_cuda_time": 0.22577998471985397, + "trace": "" + }, + "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": 44.863, + "pct_cuda_time": 0.22577998471985397, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.472, + "cuda_time_us": 8.256, + "pct_cuda_time": 0.04154959663524763, + "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": 8.256, + "pct_cuda_time": 0.04154959663524763, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 455.419, + "cuda_time_us": 440.538, + "pct_cuda_time": 2.21707560592281, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 152.859, + "cuda_time_us": 268.7, + "pct_cuda_time": 1.3522742994054067, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 267.964, + "pct_cuda_time": 1.3485702655968381, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.65, + "cuda_time_us": 34.112, + "pct_cuda_time": 0.17167391477974409, + "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": 34.112, + "pct_cuda_time": 0.17167391477974409, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 152.432, + "cuda_time_us": 137.726, + "pct_cuda_time": 0.6931273917376592, + "trace": "" + }, + "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": 137.726, + "pct_cuda_time": 0.6931273917376592, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2639.138, + "cuda_time_us": 605.69, + "pct_cuda_time": 3.0482285835759617, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.119, + "cuda_time_us": 8.096, + "pct_cuda_time": 0.040744371894254464, + "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": 8.096, + "pct_cuda_time": 0.040744371894254464, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1954.434, + "cuda_time_us": 146.112, + "pct_cuda_time": 0.7353312334749639, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 152.619, + "cuda_time_us": 68.512, + "pct_cuda_time": 0.3447972340932759, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.776, + "pct_cuda_time": 0.34109320028470724, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 515.994, + "cuda_time_us": 10.913, + "pct_cuda_time": 0.05492135999036547, + "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": 10.913, + "pct_cuda_time": 0.05492135999036547, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 939.118, + "cuda_time_us": 21.887, + "pct_cuda_time": 0.1101497119132346, + "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": 5.215, + "pct_cuda_time": 0.02624529390174617, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.2, + "pct_cuda_time": 0.07649635039435125, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.007408067617137174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 186.694, + "cuda_time_us": 44.8, + "pct_cuda_time": 0.22546292747808788, + "trace": "" + }, + "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": 44.8, + "pct_cuda_time": 0.22546292747808788, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.996, + "cuda_time_us": 8.352, + "pct_cuda_time": 0.04203273147984353, + "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": 8.352, + "pct_cuda_time": 0.04203273147984353, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 458.417, + "cuda_time_us": 443.13, + "pct_cuda_time": 2.2301202467268992, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.927, + "cuda_time_us": 270.652, + "pct_cuda_time": 1.3620980412455235, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 269.916, + "pct_cuda_time": 1.3583940074369547, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.995, + "cuda_time_us": 33.6, + "pct_cuda_time": 0.16909719560856595, + "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": 33.6, + "pct_cuda_time": 0.16909719560856595, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.339, + "cuda_time_us": 138.878, + "pct_cuda_time": 0.69892500987281, + "trace": "" + }, + "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": 138.878, + "pct_cuda_time": 0.69892500987281, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2441.899, + "cuda_time_us": 607.1289999999999, + "pct_cuda_time": 3.055470573590268, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.257, + "cuda_time_us": 8.416, + "pct_cuda_time": 0.0423548213762408, + "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": 8.416, + "pct_cuda_time": 0.0423548213762408, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1730.66, + "cuda_time_us": 146.367, + "pct_cuda_time": 0.7366145604059217, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 152.223, + "cuda_time_us": 68.576, + "pct_cuda_time": 0.3451193239896731, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.704, + "pct_cuda_time": 0.0035429888603699528, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.872, + "pct_cuda_time": 0.3415763351293032, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 527.63, + "cuda_time_us": 11.007, + "pct_cuda_time": 0.055394429525698965, + "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": 11.007, + "pct_cuda_time": 0.055394429525698965, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 728.953, + "cuda_time_us": 21.568, + "pct_cuda_time": 0.10854429508587947, + "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": 5.248, + "pct_cuda_time": 0.026411371504576012, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.04, + "pct_cuda_time": 0.07569112565335809, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.006441797927945369, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 176.247, + "cuda_time_us": 45.216, + "pct_cuda_time": 0.22755651180467015, + "trace": "" + }, + "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": 45.216, + "pct_cuda_time": 0.22755651180467015, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 98.91, + "cuda_time_us": 8.287, + "pct_cuda_time": 0.041705608928815065, + "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": 8.287, + "pct_cuda_time": 0.041705608928815065, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 468.768, + "cuda_time_us": 444.05899999999997, + "pct_cuda_time": 2.2347955828792907, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.104, + "cuda_time_us": 270.876, + "pct_cuda_time": 1.3632253558829137, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 270.14, + "pct_cuda_time": 1.3595213220743452, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.609, + "cuda_time_us": 33.664, + "pct_cuda_time": 0.1694192855049632, + "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": 33.664, + "pct_cuda_time": 0.1694192855049632, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.701, + "cuda_time_us": 139.519, + "pct_cuda_time": 0.702150941491414, + "trace": "" + }, + "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": 139.519, + "pct_cuda_time": 0.702150941491414, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2397.654, + "cuda_time_us": 607.513, + "pct_cuda_time": 3.0574031129686525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.442, + "cuda_time_us": 8.352, + "pct_cuda_time": 0.04203273147984353, + "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": 8.352, + "pct_cuda_time": 0.04203273147984353, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1719.478, + "cuda_time_us": 146.751, + "pct_cuda_time": 0.7385470997843053, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.307, + "cuda_time_us": 69.60000000000001, + "pct_cuda_time": 0.3502727623320295, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 68.864, + "pct_cuda_time": 0.3465687285234609, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 511.489, + "cuda_time_us": 10.559, + "pct_cuda_time": 0.05313980025091808, + "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": 10.559, + "pct_cuda_time": 0.05313980025091808, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 743.115, + "cuda_time_us": 21.472, + "pct_cuda_time": 0.10806116024128358, + "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": 5.152, + "pct_cuda_time": 0.02592823665998011, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.04, + "pct_cuda_time": 0.07569112565335809, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.006441797927945369, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 171.146, + "cuda_time_us": 45.12, + "pct_cuda_time": 0.22707337696007426, + "trace": "" + }, + "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": 45.12, + "pct_cuda_time": 0.22707337696007426, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.634, + "cuda_time_us": 8.16, + "pct_cuda_time": 0.04106646179065173, + "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": 8.16, + "pct_cuda_time": 0.04106646179065173, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 454.236, + "cuda_time_us": 444.25, + "pct_cuda_time": 2.235756819913852, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.123, + "cuda_time_us": 270.3, + "pct_cuda_time": 1.3603265468153385, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 269.564, + "pct_cuda_time": 1.35662251300677, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.639, + "cuda_time_us": 34.24, + "pct_cuda_time": 0.17231809457253863, + "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": 34.24, + "pct_cuda_time": 0.17231809457253863, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 135.955, + "cuda_time_us": 139.71, + "pct_cuda_time": 0.7031121785259746, + "trace": "" + }, + "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": 139.71, + "pct_cuda_time": 0.7031121785259746, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2573.032, + "cuda_time_us": 610.038, + "pct_cuda_time": 3.070110565912451, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.776, + "cuda_time_us": 8.512, + "pct_cuda_time": 0.042837956220836707, + "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": 8.512, + "pct_cuda_time": 0.042837956220836707, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1865.002, + "cuda_time_us": 147.229, + "pct_cuda_time": 0.7409527086980225, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.6, + "cuda_time_us": 69.15100000000001, + "pct_cuda_time": 0.3480131004026174, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 68.415, + "pct_cuda_time": 0.3443090665940488, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 527.007, + "cuda_time_us": 10.56, + "pct_cuda_time": 0.05314483290554929, + "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": 10.56, + "pct_cuda_time": 0.05314483290554929, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 829.244, + "cuda_time_us": 22.239, + "pct_cuda_time": 0.1119212063434196, + "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": 5.536, + "pct_cuda_time": 0.02786077603836372, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.231, + "pct_cuda_time": 0.07665236268791868, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.007408067617137174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 207.551, + "cuda_time_us": 45.279, + "pct_cuda_time": 0.22787356904643624, + "trace": "" + }, + "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": 45.279, + "pct_cuda_time": 0.22787356904643624, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.023, + "cuda_time_us": 8.256, + "pct_cuda_time": 0.04154959663524763, + "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": 8.256, + "pct_cuda_time": 0.04154959663524763, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 482.797, + "cuda_time_us": 446.041, + "pct_cuda_time": 2.244770304358344, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 170.675, + "cuda_time_us": 272.348, + "pct_cuda_time": 1.370633423500051, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 271.612, + "pct_cuda_time": 1.3669293896914827, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.837, + "cuda_time_us": 33.599, + "pct_cuda_time": 0.1690921629539347, + "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": 33.599, + "pct_cuda_time": 0.1690921629539347, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.928, + "cuda_time_us": 140.094, + "pct_cuda_time": 0.7050447179043582, + "trace": "" + }, + "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": 140.094, + "pct_cuda_time": 0.7050447179043582, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2389.084, + "cuda_time_us": 606.5859999999999, + "pct_cuda_time": 3.0527378421255227, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.327, + "cuda_time_us": 8.033, + "pct_cuda_time": 0.040427314652488396, + "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": 8.033, + "pct_cuda_time": 0.040427314652488396, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1709.914, + "cuda_time_us": 146.335, + "pct_cuda_time": 0.7364535154577232, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.023, + "cuda_time_us": 68.384, + "pct_cuda_time": 0.3441530543004813, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.003709066463199794, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 67.647, + "pct_cuda_time": 0.3404439878372816, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 510.675, + "cuda_time_us": 10.944, + "pct_cuda_time": 0.055077372283932904, + "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": 10.944, + "pct_cuda_time": 0.055077372283932904, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 719.653, + "cuda_time_us": 21.92, + "pct_cuda_time": 0.11031578951606447, + "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": 5.248, + "pct_cuda_time": 0.026411371504576012, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.2, + "pct_cuda_time": 0.07649635039435125, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.007408067617137174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 173.862, + "cuda_time_us": 45.087, + "pct_cuda_time": 0.22690729935724444, + "trace": "" + }, + "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": 45.087, + "pct_cuda_time": 0.22690729935724444, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.873, + "cuda_time_us": 8.48, + "pct_cuda_time": 0.04267691127263807, + "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": 8.48, + "pct_cuda_time": 0.04267691127263807, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 455.271, + "cuda_time_us": 443.73799999999994, + "pct_cuda_time": 2.2331801007426733, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.814, + "cuda_time_us": 270.876, + "pct_cuda_time": 1.3632253558829137, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 270.14, + "pct_cuda_time": 1.3595213220743452, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.9, + "cuda_time_us": 33.856, + "pct_cuda_time": 0.17038555519415502, + "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": 33.856, + "pct_cuda_time": 0.17038555519415502, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 139.294, + "cuda_time_us": 139.006, + "pct_cuda_time": 0.6995691896656047, + "trace": "" + }, + "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": 139.006, + "pct_cuda_time": 0.6995691896656047, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2480.019, + "cuda_time_us": 608.216, + "pct_cuda_time": 3.0609410691743912, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.583, + "cuda_time_us": 8.64, + "pct_cuda_time": 0.04348213601363124, + "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": 8.64, + "pct_cuda_time": 0.04348213601363124, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1760.307, + "cuda_time_us": 146.847, + "pct_cuda_time": 0.7390302346289013, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.264, + "cuda_time_us": 68.89500000000001, + "pct_cuda_time": 0.3467247408170283, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 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": 68.159, + "pct_cuda_time": 0.3430207070084597, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[768, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 518.342, + "cuda_time_us": 10.528, + "pct_cuda_time": 0.05298378795735066, + "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": 10.528, + "pct_cuda_time": 0.05298378795735066, + "trace": "_C::rotary_embedding(int64[768], bfloat16[768, 4096], bfloat16[768, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 735.498, + "cuda_time_us": 21.952, + "pct_cuda_time": 0.1104768344642631, + "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": 5.248, + "pct_cuda_time": 0.026411371504576012, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[768], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 15.424, + "pct_cuda_time": 0.07762366503174169, + "trace": "_vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 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": 0.006441797927945369, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], None, None, bfloat16[768, 32, 128], int32[7], int32[7], 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[768, 32, 128], bfloat16[768, 8, 128], bfloat16[768, 8, 128], bfloat16[768, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 195.526, + "cuda_time_us": 45.472, + "pct_cuda_time": 0.22884487139025922, + "trace": "" + }, + "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": 45.472, + "pct_cuda_time": 0.22884487139025922, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[768, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 106.508, + "cuda_time_us": 8.223, + "pct_cuda_time": 0.04138351903241779, + "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": 8.223, + "pct_cuda_time": 0.04138351903241779, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 473.075, + "cuda_time_us": 444.506, + "pct_cuda_time": 2.237045179499441, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 167.863, + "cuda_time_us": 271.772, + "pct_cuda_time": 1.3677346144324756, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 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": 271.036, + "pct_cuda_time": 1.364030580623907, + "trace": "mm(bfloat16[768, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[768, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[768, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.021, + "cuda_time_us": 33.664, + "pct_cuda_time": 0.1694192855049632, + "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": 33.664, + "pct_cuda_time": 0.1694192855049632, + "trace": "_C::silu_and_mul(bfloat16[768, 14336], bfloat16[768, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 143.175, + "cuda_time_us": 139.07, + "pct_cuda_time": 0.6998912795620019, + "trace": "" + }, + "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": 139.07, + "pct_cuda_time": 0.6998912795620019, + "trace": "mm(bfloat16[768, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[768, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[768, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.089, + "cuda_time_us": 8.64, + "pct_cuda_time": 0.04348213601363124, + "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": 8.64, + "pct_cuda_time": 0.04348213601363124, + "trace": "_C::fused_add_rms_norm(bfloat16[768, 4096], bfloat16[768, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 459.467, + "cuda_time_us": 351.419, + "pct_cuda_time": 1.768570457844245, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 5.023, + "pct_cuda_time": 0.025279024212554364, + "trace": "index_select(bfloat16[768, 4096], 0, int64[6])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.003709066463199794, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[6, 4096], bfloat16[128256, 4096], None)" + }, + "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": 345.659, + "pct_cuda_time": 1.7395823671684907, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[6, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 3499.821, + "cuda_time_us": 118.525, + "pct_cuda_time": 0.5964953901638476, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.003704033808568587, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 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": 0.003704033808568587, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 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": 0.0038650787567672215, + "trace": "copy_(int32[6], int32[6], True) <- _to_copy(int32[6], 3, 0, None, None, True, None) <- to(int32[6], 3, 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": 0.0038650787567672215, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 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": 0.0038650787567672215, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 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": 0.004026123704965856, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 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": 0.004026123704965856, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 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": 4.544, + "pct_cuda_time": 0.022868382644206056, + "trace": "copy_(float32[6, 128256], bfloat16[6, 128256], False) <- _to_copy(bfloat16[6, 128256], 6, None, None, None, False, None) <- to(bfloat16[6, 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": 5.536, + "pct_cuda_time": 0.02786077603836372, + "trace": "div_(float32[6, 128256], bfloat16[6, 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": 35.039, + "pct_cuda_time": 0.17633918562287326, + "trace": "_softmax(float32[6, 128256], -1, False) <- softmax(float32[6, 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": 28.607, + "pct_cuda_time": 0.14396915103494778, + "trace": "_log_softmax(float32[6, 128256], -1, False) <- log_softmax(float32[6, 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": 1.952, + "pct_cuda_time": 0.009823741840116688, + "trace": "copy_(int64[6], int32[6], False) <- _to_copy(int32[6], 4, None, None, None, False, None) <- to(int32[6], 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": 6.208, + "pct_cuda_time": 0.031242719950535038, + "trace": "index(float32[6, 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": 28.191, + "pct_cuda_time": 0.14187556670836554, + "trace": "argmax(float32[6, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.015460315027068886, + "trace": "copy_(int64[6], int64[6], False) <- _to_copy(int64[6], 4, 0, None, None, False, None) <- to(int64[6], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + }, + "decode_1": { + "metadata": { + "num_running_seqs": 6 + }, + "summary_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cuda_time_us": 6389.298, + "pct_cuda_time": 93.07273406358541, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cuda_time_us": 6.048, + "pct_cuda_time": 0.08810105517328581, + "invocations": 1 + }, + "children": [ + { + "entry": { 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vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cuda_time_us": 194.53199999999998, + "pct_cuda_time": 2.833742471059794, + "invocations": 63 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cuda_time_us": 1834.019, + "pct_cuda_time": 26.716106003282814, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cuda_time_us": 665.1420000000002, + "pct_cuda_time": 9.689105826731097, + "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": 665.1420000000002, + "pct_cuda_time": 9.689105826731097, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cuda_time_us": 118.36500000000004, + "pct_cuda_time": 1.7242198074712265, + 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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": 358.7809999999999, + "pct_cuda_time": 5.226353286396601, + "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": 41.853, + "pct_cuda_time": 0.6096715380568007, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cuda_time_us": 568.985, + "pct_cuda_time": 8.288389364710982, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cuda_time_us": 501.178, + "pct_cuda_time": 7.300646598815646, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cuda_time_us": 67.80700000000003, + "pct_cuda_time": 0.9877427658953362, + "invocations": 32 + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cuda_time_us": 4347.403, + "pct_cuda_time": 63.32850389608271, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cuda_time_us": 2664.4130000000005, + "pct_cuda_time": 38.81243331967923, + "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": 2664.4130000000005, + "pct_cuda_time": 38.81243331967923, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cuda_time_us": 289.72599999999994, + "pct_cuda_time": 4.220430937687731, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cuda_time_us": 289.72599999999994, + "pct_cuda_time": 4.220430937687731, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cuda_time_us": 1393.264, + "pct_cuda_time": 20.295639638715752, + "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": 1393.264, + "pct_cuda_time": 20.295639638715752, + "invocations": 32 + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 3.04, + "pct_cuda_time": 0.0442835991611754, + "invocations": 1 + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cuda_time_us": 350.619, + "pct_cuda_time": 5.107457649438211, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cuda_time_us": 4.768, + "pct_cuda_time": 0.06945532921068563, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cuda_time_us": 0.768, + "pct_cuda_time": 0.011187435577560101, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cuda_time_us": 345.083, + "pct_cuda_time": 5.026814884649965, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cuda_time_us": 124.92699999999999, + "pct_cuda_time": 1.819808286976368, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cuda_time_us": 13.184, + "pct_cuda_time": 0.19205097741478175, + "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": 4.416, + "pct_cuda_time": 0.06432775457097059, + "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": 5.504, + "pct_cuda_time": 0.08017662163918073, + "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": 34.943, + "pct_cuda_time": 0.5090137518055763, + "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": 28.384, + "pct_cuda_time": 0.41346897322065873, + "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": 1.824, + "pct_cuda_time": 0.026570159496705242, + "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": 6.208, + "pct_cuda_time": 0.09043177091861082, + "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": 27.776, + "pct_cuda_time": 0.40461225338842366, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cuda_time_us": 2.688, + "pct_cuda_time": 0.03915602452146036, + "invocations": 1 + }, + "children": [] + } + ] + } + ], + "model_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cpu_time_us": 84543.208, + "cuda_time_us": 6389.298, + "pct_cuda_time": 93.07273406358541, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cpu_time_us": 412.114, + "cuda_time_us": 6.048, + "pct_cuda_time": 0.08810105517328581, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 6.048, + "pct_cuda_time": 0.08810105517328581, + "trace": "index_select(bfloat16[128256, 4096], 0, int64[6]) <- embedding(bfloat16[128256, 4096], int64[6], -1, False, False)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 5617.006, + "cuda_time_us": 206.52300000000002, + "pct_cuda_time": 3.0084150491984962, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 405.089, + "cuda_time_us": 4.256, + "pct_cuda_time": 0.06199703882564557, + "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": 4.256, + "pct_cuda_time": 0.06199703882564557, + "trace": "_C::rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 4166.876, + "cuda_time_us": 62.269, + "pct_cuda_time": 0.9070708671602734, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 850.843, + "cuda_time_us": 25.055, + "pct_cuda_time": 0.36497551874449, + "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": 25.055, + "pct_cuda_time": 0.36497551874449, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 1204.019, + "cuda_time_us": 3.583, + "pct_cuda_time": 0.052193465721872195, + "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": 3.583, + "pct_cuda_time": 0.052193465721872195, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1363.045, + "cuda_time_us": 14.847, + "pct_cuda_time": 0.2162758541927537, + "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": 2.368, + "pct_cuda_time": 0.03449459303081031, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.167, + "pct_cuda_time": 0.1626693920502782, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.019111869111665174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 360.603, + "cuda_time_us": 18.784, + "pct_cuda_time": 0.27362602850115747, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.704, + "pct_cuda_time": 0.2433267238119322, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.08, + "pct_cuda_time": 0.030299304689225277, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 151.749, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04520131848589713, + "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": 3.103, + "pct_cuda_time": 0.04520131848589713, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 741.937, + "cuda_time_us": 136.895, + "pct_cuda_time": 1.99414582472668, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 267.14, + "cuda_time_us": 83.551, + "pct_cuda_time": 1.2170851952353179, + "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": 83.551, + "pct_cuda_time": 1.2170851952353179, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 207.194, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.13145236803633117, + "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": 0.13145236803633117, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 174.569, + "cuda_time_us": 44.32, + "pct_cuda_time": 0.6456082614550308, + "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": 44.32, + "pct_cuda_time": 0.6456082614550308, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2799.497, + "cuda_time_us": 198.75, + "pct_cuda_time": 2.8951859648959246, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.621, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044749742310240405, + "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": 0.044749742310240405, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2037.352, + "cuda_time_us": 57.6, + "pct_cuda_time": 0.8390576683170077, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 147.159, + "cuda_time_us": 21.408, + "pct_cuda_time": 0.3118497667244879, + "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": 21.408, + "pct_cuda_time": 0.3118497667244879, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 582.658, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05360646214247549, + "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": 0.05360646214247549, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 935.956, + "cuda_time_us": 15.008, + "pct_cuda_time": 0.21862113691148696, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.2, + "pct_cuda_time": 0.16315010217275147, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 181.683, + "cuda_time_us": 17.503999999999998, + "pct_cuda_time": 0.2549803025385573, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.392, + "pct_cuda_time": 0.22421485470026703, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.159, + "cuda_time_us": 3.264, + "pct_cuda_time": 0.04754660120463043, + "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": 0.04754660120463043, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 501.274, + "cuda_time_us": 134.814, + "pct_cuda_time": 1.9638319530640462, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 178.437, + "cuda_time_us": 82.687, + "pct_cuda_time": 1.2044993302105627, + "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": 82.687, + "pct_cuda_time": 1.2044993302105627, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 109.761, + "cuda_time_us": 8.959, + "pct_cuda_time": 0.1305055147647929, + "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": 8.959, + "pct_cuda_time": 0.1305055147647929, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.617, + "cuda_time_us": 43.168, + "pct_cuda_time": 0.6288271080886907, + "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": 43.168, + "pct_cuda_time": 0.6288271080886907, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2466.437, + "cuda_time_us": 199.038, + "pct_cuda_time": 2.89938125323751, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.822, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1733.674, + "cuda_time_us": 56.607, + "pct_cuda_time": 0.8245926637225842, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 141.946, + "cuda_time_us": 20.384, + "pct_cuda_time": 0.2969331859544077, + "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": 20.384, + "pct_cuda_time": 0.2969331859544077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 524.338, + "cuda_time_us": 3.679, + "pct_cuda_time": 0.053591895169067205, + "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": 3.679, + "pct_cuda_time": 0.053591895169067205, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 742.643, + "cuda_time_us": 14.975999999999999, + "pct_cuda_time": 0.21815499376242198, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.168, + "pct_cuda_time": 0.1626839590236865, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 168.295, + "cuda_time_us": 17.567999999999998, + "pct_cuda_time": 0.25591258883668727, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.456, + "pct_cuda_time": 0.22514714099839706, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.037, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.045230452432713696, + "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": 0.045230452432713696, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 494.051, + "cuda_time_us": 136.286, + "pct_cuda_time": 1.9852745379210366, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.436, + "cuda_time_us": 83.103, + "pct_cuda_time": 1.2105591911484077, + "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": 83.103, + "pct_cuda_time": 1.2105591911484077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.566, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.1319185111853962, + "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": 0.1319185111853962, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 167.744, + "cuda_time_us": 44.127, + "pct_cuda_time": 0.6427968355872327, + "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": 44.127, + "pct_cuda_time": 0.6427968355872327, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2495.568, + "cuda_time_us": 198.749, + "pct_cuda_time": 2.895171397922516, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.829, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1773.174, + "cuda_time_us": 56.672, + "pct_cuda_time": 0.8255395169941224, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.47, + "cuda_time_us": 20.416, + "pct_cuda_time": 0.2973993291034727, + "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": 20.416, + "pct_cuda_time": 0.2973993291034727, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 519.722, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.054072605291540496, + "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": 0.054072605291540496, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 774.855, + "cuda_time_us": 14.975999999999999, + "pct_cuda_time": 0.21815499376242198, + "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": 0.03635916562707033, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.2, + "pct_cuda_time": 0.16315010217275147, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 164.792, + "cuda_time_us": 17.567999999999998, + "pct_cuda_time": 0.25591258883668727, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.456, + "pct_cuda_time": 0.22514714099839706, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 91.585, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04521588545930541, + "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": 0.04521588545930541, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 469.347, + "cuda_time_us": 135.933, + "pct_cuda_time": 1.980132396307913, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.735, + "cuda_time_us": 83.774, + "pct_cuda_time": 1.2203336303053645, + "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": 83.774, + "pct_cuda_time": 1.2203336303053645, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.825, + "cuda_time_us": 8.864, + "pct_cuda_time": 0.12912165229100617, + "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": 8.864, + "pct_cuda_time": 0.12912165229100617, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 145.907, + "cuda_time_us": 43.295, + "pct_cuda_time": 0.6306771137115424, + "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": 43.295, + "pct_cuda_time": 0.6306771137115424, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2407.614, + "cuda_time_us": 199.39100000000002, + "pct_cuda_time": 2.9045233948506333, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.132, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.0438174560121104, + "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": 0.0438174560121104, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1718.45, + "cuda_time_us": 57.44, + "pct_cuda_time": 0.8367269525716826, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 141.936, + "cuda_time_us": 20.608, + "pct_cuda_time": 0.30019618799786274, + "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": 20.608, + "pct_cuda_time": 0.30019618799786274, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 481.606, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053140318993410485, + "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": 0.053140318993410485, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 774.824, + "cuda_time_us": 14.911999999999999, + "pct_cuda_time": 0.21722270746429195, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.104, + "pct_cuda_time": 0.16175167272555646, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 160.244, + "cuda_time_us": 18.272, + "pct_cuda_time": 0.26616773811611744, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.16, + "pct_cuda_time": 0.23540229027782714, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.63, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.045682028608370416, + "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": 0.045682028608370416, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 457.791, + "cuda_time_us": 135.80700000000002, + "pct_cuda_time": 1.9782969576584701, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 161.072, + "cuda_time_us": 82.911, + "pct_cuda_time": 1.2077623322540179, + "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": 82.911, + "pct_cuda_time": 1.2077623322540179, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.511, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.1305200817382012, + "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": 8.96, + "pct_cuda_time": 0.1305200817382012, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.227, + "cuda_time_us": 43.936, + "pct_cuda_time": 0.6400145436662509, + "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": 43.936, + "pct_cuda_time": 0.6400145436662509, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2566.144, + "cuda_time_us": 198.844, + "pct_cuda_time": 2.896555260396303, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.867, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.043351312863045395, + "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": 0.043351312863045395, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1844.153, + "cuda_time_us": 57.053, + "pct_cuda_time": 0.8310895338626776, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 134.178, + "cuda_time_us": 20.416, + "pct_cuda_time": 0.2973993291034727, + "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": 20.416, + "pct_cuda_time": 0.2973993291034727, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 483.374, + "cuda_time_us": 3.807, + "pct_cuda_time": 0.05545646776532723, + "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": 3.807, + "pct_cuda_time": 0.05545646776532723, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 900.974, + "cuda_time_us": 15.166999999999998, + "pct_cuda_time": 0.2209372856834037, + "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": 2.687, + "pct_cuda_time": 0.039141457548052074, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.2, + "pct_cuda_time": 0.16315010217275147, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 175.059, + "cuda_time_us": 17.663, + "pct_cuda_time": 0.2572964513104741, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.52, + "pct_cuda_time": 0.22607942729652705, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.143, + "pct_cuda_time": 0.031217024013947, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.501, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.045682028608370416, + "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": 0.045682028608370416, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.572, + "cuda_time_us": 135.679, + "pct_cuda_time": 1.9764323850622096, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 168.959, + "cuda_time_us": 82.815, + "pct_cuda_time": 1.2063639028068225, + "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": 82.815, + "pct_cuda_time": 1.2063639028068225, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.199, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.1328507974835262, + "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": 0.1328507974835262, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 147.773, + "cuda_time_us": 43.744, + "pct_cuda_time": 0.6372176847718608, + "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": 43.744, + "pct_cuda_time": 0.6372176847718608, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2466.917, + "cuda_time_us": 199.486, + "pct_cuda_time": 2.9059072573244196, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.967, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04521588545930541, + "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": 0.04521588545930541, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1777.985, + "cuda_time_us": 57.791, + "pct_cuda_time": 0.8418399602379892, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.588, + "cuda_time_us": 20.543, + "pct_cuda_time": 0.2992493347263244, + "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": 20.543, + "pct_cuda_time": 0.2992493347263244, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 516.506, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053140318993410485, + "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": 0.053140318993410485, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 762.815, + "cuda_time_us": 15.136, + "pct_cuda_time": 0.22048570950774699, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.328, + "pct_cuda_time": 0.1650146747690115, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 163.766, + "cuda_time_us": 18.464, + "pct_cuda_time": 0.26896459701050746, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.32, + "pct_cuda_time": 0.23773300602315217, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.144, + "pct_cuda_time": 0.031231590987355288, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.265, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04614817175743542, + "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": 0.04614817175743542, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 462.971, + "cuda_time_us": 135.423, + "pct_cuda_time": 1.9727032398696898, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 161.728, + "cuda_time_us": 82.4, + "pct_cuda_time": 1.2003186088423858, + "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": 82.4, + "pct_cuda_time": 1.2003186088423858, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.468, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.13145236803633117, + "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": 0.13145236803633117, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 145.296, + "cuda_time_us": 43.999, + "pct_cuda_time": 0.6409322629909726, + "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": 43.999, + "pct_cuda_time": 0.6409322629909726, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2379.871, + "cuda_time_us": 198.07899999999998, + "pct_cuda_time": 2.8854115257389674, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.302, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04521588545930541, + "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": 0.04521588545930541, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1666.146, + "cuda_time_us": 56.833, + "pct_cuda_time": 0.8278847997128558, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.244, + "cuda_time_us": 20.543, + "pct_cuda_time": 0.2992493347263244, + "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": 20.543, + "pct_cuda_time": 0.2992493347263244, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 480.732, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.0550048915896705, + "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": 0.0550048915896705, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 738.98, + "cuda_time_us": 14.976999999999999, + "pct_cuda_time": 0.21816956073583024, + "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": 0.03637373260047861, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.2, + "pct_cuda_time": 0.16315010217275147, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 155.588, + "cuda_time_us": 17.537, + "pct_cuda_time": 0.25546101266103055, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.424, + "pct_cuda_time": 0.22468099784933204, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.113, + "pct_cuda_time": 0.030780014811698564, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 100.855, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 466.415, + "cuda_time_us": 135.10199999999998, + "pct_cuda_time": 1.9680272414056308, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 162.168, + "cuda_time_us": 82.815, + "pct_cuda_time": 1.2063639028068225, + "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": 82.815, + "pct_cuda_time": 1.2063639028068225, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.401, + "cuda_time_us": 9.088, + "pct_cuda_time": 0.1323846543344612, + "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": 9.088, + "pct_cuda_time": 0.1323846543344612, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 150.025, + "cuda_time_us": 43.199, + "pct_cuda_time": 0.6292786842643474, + "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": 43.199, + "pct_cuda_time": 0.6292786842643474, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2281.26, + "cuda_time_us": 200.414, + "pct_cuda_time": 2.919425408647305, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.154, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1609.567, + "cuda_time_us": 57.344, + "pct_cuda_time": 0.8353285231244876, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.569, + "cuda_time_us": 20.896, + "pct_cuda_time": 0.3043914763394478, + "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": 20.896, + "pct_cuda_time": 0.3043914763394478, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 462.379, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.0545387484406055, + "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": 0.0545387484406055, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 716.644, + "cuda_time_us": 14.975999999999999, + "pct_cuda_time": 0.21815499376242198, + "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": 0.03635916562707033, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.2, + "pct_cuda_time": 0.16315010217275147, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 150.088, + "cuda_time_us": 17.728, + "pct_cuda_time": 0.25824330458201233, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.22794399989278707, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.08, + "pct_cuda_time": 0.030299304689225277, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.091, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.047080458055565426, + "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": 0.047080458055565426, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 462.527, + "cuda_time_us": 136.798, + "pct_cuda_time": 1.9927328283060768, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 150.788, + "cuda_time_us": 83.519, + "pct_cuda_time": 1.216619052086253, + "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": 83.519, + "pct_cuda_time": 1.216619052086253, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.049, + "cuda_time_us": 9.216, + "pct_cuda_time": 0.13424922693072122, + "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": 0.13424922693072122, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 160.724, + "cuda_time_us": 44.063, + "pct_cuda_time": 0.6418645492891026, + "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": 44.063, + "pct_cuda_time": 0.6418645492891026, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2499.41, + "cuda_time_us": 199.489, + "pct_cuda_time": 2.9059509582446448, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.224, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.045230452432713696, + "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": 0.045230452432713696, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1815.47, + "cuda_time_us": 56.896, + "pct_cuda_time": 0.8288025190375775, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 212.443, + "cuda_time_us": 20.352, + "pct_cuda_time": 0.2964670428053427, + "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": 20.352, + "pct_cuda_time": 0.2964670428053427, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 506.342, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.0545387484406055, + "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": 0.0545387484406055, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 771.524, + "cuda_time_us": 15.008, + "pct_cuda_time": 0.21862113691148696, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.2, + "pct_cuda_time": 0.16315010217275147, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 160.329, + "cuda_time_us": 17.792, + "pct_cuda_time": 0.25917559088014236, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.68, + "pct_cuda_time": 0.2284101430418521, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.082, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 454.093, + "cuda_time_us": 136.448, + "pct_cuda_time": 1.9876343876131783, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.472, + "cuda_time_us": 83.615, + "pct_cuda_time": 1.2180174815334477, + "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": 83.615, + "pct_cuda_time": 1.2180174815334477, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.364, + "cuda_time_us": 9.665, + "pct_cuda_time": 0.14078979799103955, + "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": 9.665, + "pct_cuda_time": 0.14078979799103955, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.795, + "cuda_time_us": 43.168, + "pct_cuda_time": 0.6288271080886907, + "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": 43.168, + "pct_cuda_time": 0.6288271080886907, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2330.797, + "cuda_time_us": 198.687, + "pct_cuda_time": 2.894268245571203, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.503, + "cuda_time_us": 3.041, + "pct_cuda_time": 0.044298166134583684, + "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": 3.041, + "pct_cuda_time": 0.044298166134583684, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1662.34, + "cuda_time_us": 57.056, + "pct_cuda_time": 0.8311332347829026, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.5, + "cuda_time_us": 20.608, + "pct_cuda_time": 0.30019618799786274, + "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": 20.608, + "pct_cuda_time": 0.30019618799786274, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 484.112, + "cuda_time_us": 3.647, + "pct_cuda_time": 0.0531257520200022, + "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": 3.647, + "pct_cuda_time": 0.0531257520200022, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 726.213, + "cuda_time_us": 14.977, + "pct_cuda_time": 0.21816956073583024, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.169, + "pct_cuda_time": 0.16269852599709475, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 151.172, + "cuda_time_us": 17.823999999999998, + "pct_cuda_time": 0.2596417340292073, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.552, + "pct_cuda_time": 0.22654557044559206, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.272, + "pct_cuda_time": 0.0330961635836153, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.137, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044749742310240405, + "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": 0.044749742310240405, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 452.582, + "cuda_time_us": 135.518, + "pct_cuda_time": 1.9740871023434765, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 155.068, + "cuda_time_us": 83.199, + "pct_cuda_time": 1.2119576205956026, + "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": 83.199, + "pct_cuda_time": 1.2119576205956026, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 105.453, + "cuda_time_us": 8.864, + "pct_cuda_time": 0.12912165229100617, + "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": 8.864, + "pct_cuda_time": 0.12912165229100617, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.031, + "cuda_time_us": 43.455, + "pct_cuda_time": 0.6330078294568674, + "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": 43.455, + "pct_cuda_time": 0.6330078294568674, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2470.545, + "cuda_time_us": 199.26000000000002, + "pct_cuda_time": 2.902615121334149, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.202, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04521588545930541, + "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": 0.04521588545930541, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1767.752, + "cuda_time_us": 57.342, + "pct_cuda_time": 0.8352993891776711, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.88, + "cuda_time_us": 20.512, + "pct_cuda_time": 0.2987977585506677, + "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": 20.512, + "pct_cuda_time": 0.2987977585506677, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 464.977, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.054072605291540496, + "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": 0.054072605291540496, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 741.145, + "cuda_time_us": 15.103, + "pct_cuda_time": 0.2200049993852737, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.263, + "pct_cuda_time": 0.1640678214974732, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.019111869111665174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 168.312, + "cuda_time_us": 18.015, + "pct_cuda_time": 0.2624240259501891, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.903, + "pct_cuda_time": 0.23165857811189883, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.493, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044749742310240405, + "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": 0.044749742310240405, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 476.982, + "cuda_time_us": 135.74200000000002, + "pct_cuda_time": 1.9773501043869317, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 168.33, + "cuda_time_us": 82.943, + "pct_cuda_time": 1.2082284754030825, + "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": 82.943, + "pct_cuda_time": 1.2082284754030825, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.868, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.1309862248872662, + "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": 0.1309862248872662, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 149.368, + "cuda_time_us": 43.807, + "pct_cuda_time": 0.6381354040965825, + "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": 43.807, + "pct_cuda_time": 0.6381354040965825, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2665.113, + "cuda_time_us": 198.781, + "pct_cuda_time": 2.8956375410715816, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.352, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1933.883, + "cuda_time_us": 57.053999999999995, + "pct_cuda_time": 0.8311041008360859, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.378, + "cuda_time_us": 20.512, + "pct_cuda_time": 0.2987977585506677, + "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": 20.512, + "pct_cuda_time": 0.2987977585506677, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 515.005, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053140318993410485, + "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": 0.053140318993410485, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 939.022, + "cuda_time_us": 14.975999999999999, + "pct_cuda_time": 0.21815499376242198, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.136, + "pct_cuda_time": 0.16221781587462147, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.019111869111665174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 173.93, + "cuda_time_us": 17.918, + "pct_cuda_time": 0.2610110295295858, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.807, + "pct_cuda_time": 0.23026014866470382, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.111, + "pct_cuda_time": 0.030750880864882, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.769, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 481.962, + "cuda_time_us": 135.64700000000002, + "pct_cuda_time": 1.975966241913145, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 170.933, + "cuda_time_us": 83.775, + "pct_cuda_time": 1.2203481972787729, + "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": 83.775, + "pct_cuda_time": 1.2203481972787729, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.114, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.1305200817382012, + "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": 8.96, + "pct_cuda_time": 0.1305200817382012, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.68, + "cuda_time_us": 42.912, + "pct_cuda_time": 0.6250979628961707, + "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": 42.912, + "pct_cuda_time": 0.6250979628961707, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2350.863, + "cuda_time_us": 198.94, + "pct_cuda_time": 2.897953689843498, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.938, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044749742310240405, + "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": 0.044749742310240405, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1692.745, + "cuda_time_us": 56.989, + "pct_cuda_time": 0.8301572475645477, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.954, + "cuda_time_us": 20.639, + "pct_cuda_time": 0.30064776417351946, + "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": 20.639, + "pct_cuda_time": 0.30064776417351946, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 481.907, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053140318993410485, + "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": 0.053140318993410485, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 759.559, + "cuda_time_us": 15.071, + "pct_cuda_time": 0.2195388562362087, + "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": 0.03635916562707033, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.263, + "pct_cuda_time": 0.1640678214974732, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.019111869111665174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 153.368, + "cuda_time_us": 17.631, + "pct_cuda_time": 0.25683030816140906, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.52, + "pct_cuda_time": 0.22607942729652705, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.111, + "pct_cuda_time": 0.030750880864882, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.44, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 445.613, + "cuda_time_us": 135.839, + "pct_cuda_time": 1.9787631008075346, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 155.883, + "cuda_time_us": 83.871, + "pct_cuda_time": 1.2217466267259678, + "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": 83.871, + "pct_cuda_time": 1.2217466267259678, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 92.78, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.1309862248872662, + "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": 0.1309862248872662, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 145.268, + "cuda_time_us": 42.976, + "pct_cuda_time": 0.6260302491943007, + "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": 42.976, + "pct_cuda_time": 0.6260302491943007, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2536.969, + "cuda_time_us": 199.036, + "pct_cuda_time": 2.8993521192906933, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.255, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1825.257, + "cuda_time_us": 56.991, + "pct_cuda_time": 0.8301863815113643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 172.241, + "cuda_time_us": 20.607, + "pct_cuda_time": 0.30018162102445445, + "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": 20.607, + "pct_cuda_time": 0.30018162102445445, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 528.326, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053140318993410485, + "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": 0.053140318993410485, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 796.856, + "cuda_time_us": 15.04, + "pct_cuda_time": 0.21908728006055198, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.232, + "pct_cuda_time": 0.16361624532181648, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 160.916, + "cuda_time_us": 17.695999999999998, + "pct_cuda_time": 0.2577771614329473, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.584, + "pct_cuda_time": 0.22701171359465708, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.733, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04661431490650043, + "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": 3.2, + "pct_cuda_time": 0.04661431490650043, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 477.89, + "cuda_time_us": 135.805, + "pct_cuda_time": 1.9782678237116533, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 168.433, + "cuda_time_us": 83.07, + "pct_cuda_time": 1.2100784810259344, + "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": 83.07, + "pct_cuda_time": 1.2100784810259344, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.827, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.1328507974835262, + "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": 0.1328507974835262, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.725, + "cuda_time_us": 43.615, + "pct_cuda_time": 0.6353385452021926, + "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": 43.615, + "pct_cuda_time": 0.6353385452021926, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2367.036, + "cuda_time_us": 199.23, + "pct_cuda_time": 2.9021781121318995, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.202, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.0438174560121104, + "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": 0.0438174560121104, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1655.114, + "cuda_time_us": 57.248, + "pct_cuda_time": 0.8339300936772925, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.836, + "cuda_time_us": 20.832, + "pct_cuda_time": 0.3034591900413178, + "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": 20.832, + "pct_cuda_time": 0.3034591900413178, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 474.476, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05360646214247549, + "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": 0.05360646214247549, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 727.114, + "cuda_time_us": 15.008, + "pct_cuda_time": 0.21862113691148696, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.2, + "pct_cuda_time": 0.16315010217275147, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 153.782, + "cuda_time_us": 17.728, + "pct_cuda_time": 0.25824330458201233, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.2274778567437221, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.554, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04614817175743542, + "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": 0.04614817175743542, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 485.23, + "cuda_time_us": 135.80599999999998, + "pct_cuda_time": 1.9782823906850613, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 176.595, + "cuda_time_us": 83.039, + "pct_cuda_time": 1.209626904850278, + "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": 83.039, + "pct_cuda_time": 1.209626904850278, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.907, + "cuda_time_us": 9.088, + "pct_cuda_time": 0.1323846543344612, + "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": 9.088, + "pct_cuda_time": 0.1323846543344612, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 156.811, + "cuda_time_us": 43.679, + "pct_cuda_time": 0.6362708315003226, + "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": 43.679, + "pct_cuda_time": 0.6362708315003226, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2424.643, + "cuda_time_us": 199.29299999999998, + "pct_cuda_time": 2.9030958314566213, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.082, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04614817175743542, + "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": 0.04614817175743542, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1699.211, + "cuda_time_us": 56.992, + "pct_cuda_time": 0.8302009484847725, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.467, + "cuda_time_us": 20.448, + "pct_cuda_time": 0.2978654722525377, + "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": 20.448, + "pct_cuda_time": 0.2978654722525377, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 495.342, + "cuda_time_us": 3.681, + "pct_cuda_time": 0.05362102911588377, + "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": 0.05362102911588377, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 757.919, + "cuda_time_us": 15.103, + "pct_cuda_time": 0.2200049993852737, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.264, + "pct_cuda_time": 0.1640823884708815, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 1.311, + "pct_cuda_time": 0.019097302138256892, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 165.15, + "cuda_time_us": 17.759999999999998, + "pct_cuda_time": 0.25870944773107735, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.22794399989278707, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 88.644, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044749742310240405, + "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": 0.044749742310240405, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 486.867, + "cuda_time_us": 136.06099999999998, + "pct_cuda_time": 1.981996968904173, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 176.411, + "cuda_time_us": 83.71, + "pct_cuda_time": 1.2194013440072344, + "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": 83.71, + "pct_cuda_time": 1.2194013440072344, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.337, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.1319185111853962, + "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": 0.1319185111853962, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 147.996, + "cuda_time_us": 43.295, + "pct_cuda_time": 0.6306771137115424, + "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": 43.295, + "pct_cuda_time": 0.6306771137115424, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2385.86, + "cuda_time_us": 198.36300000000003, + "pct_cuda_time": 2.8895485461869206, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.426, + "cuda_time_us": 3.135, + "pct_cuda_time": 0.04566746163496213, + "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": 0.04566746163496213, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1694.155, + "cuda_time_us": 56.862, + "pct_cuda_time": 0.828307241941696, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 141.172, + "cuda_time_us": 20.608, + "pct_cuda_time": 0.30019618799786274, + "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": 20.608, + "pct_cuda_time": 0.30019618799786274, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 484.554, + "cuda_time_us": 3.679, + "pct_cuda_time": 0.053591895169067205, + "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": 3.679, + "pct_cuda_time": 0.053591895169067205, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 749.591, + "cuda_time_us": 15.039, + "pct_cuda_time": 0.21907271308714368, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.232, + "pct_cuda_time": 0.16361624532181648, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018631158989191886, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 157.244, + "cuda_time_us": 17.536, + "pct_cuda_time": 0.2554464456876223, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.424, + "pct_cuda_time": 0.22468099784933204, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.212, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 462.987, + "cuda_time_us": 135.32600000000002, + "pct_cuda_time": 1.9712902434490869, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.728, + "cuda_time_us": 82.527, + "pct_cuda_time": 1.2021686144652377, + "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": 82.527, + "pct_cuda_time": 1.2021686144652377, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.46, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.1328507974835262, + "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": 0.1328507974835262, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.312, + "cuda_time_us": 43.679, + "pct_cuda_time": 0.6362708315003226, + "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": 43.679, + "pct_cuda_time": 0.6362708315003226, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2495.336, + "cuda_time_us": 198.973, + "pct_cuda_time": 2.898434399965972, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.685, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.045682028608370416, + "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": 0.045682028608370416, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1772.15, + "cuda_time_us": 56.926, + "pct_cuda_time": 0.829239528239826, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 143.535, + "cuda_time_us": 20.512, + "pct_cuda_time": 0.2987977585506677, + "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": 20.512, + "pct_cuda_time": 0.2987977585506677, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 484.265, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05593717788780051, + "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": 0.05593717788780051, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 773.572, + "cuda_time_us": 14.975, + "pct_cuda_time": 0.21814042678901369, + "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": 2.495, + "pct_cuda_time": 0.036344598653662054, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.168, + "pct_cuda_time": 0.1626839590236865, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.019111869111665174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 173.502, + "cuda_time_us": 17.599, + "pct_cuda_time": 0.25636416501234405, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.519, + "pct_cuda_time": 0.2260648603231188, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.08, + "pct_cuda_time": 0.030299304689225277, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.161, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044749742310240405, + "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": 0.044749742310240405, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 489.016, + "cuda_time_us": 135.839, + "pct_cuda_time": 1.9787631008075346, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 171.787, + "cuda_time_us": 83.327, + "pct_cuda_time": 1.2138221931918627, + "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": 83.327, + "pct_cuda_time": 1.2138221931918627, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.629, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.1309862248872662, + "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": 0.1309862248872662, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.06, + "cuda_time_us": 43.52, + "pct_cuda_time": 0.6339546827284058, + "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": 43.52, + "pct_cuda_time": 0.6339546827284058, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2721.419, + "cuda_time_us": 199.35999999999999, + "pct_cuda_time": 2.9040718186749763, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.062, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1811.053, + "cuda_time_us": 57.248, + "pct_cuda_time": 0.8339300936772925, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 167.021, + "cuda_time_us": 20.576, + "pct_cuda_time": 0.2997300448487977, + "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": 20.576, + "pct_cuda_time": 0.2997300448487977, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 506.855, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.054072605291540496, + "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": 0.054072605291540496, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 808.751, + "cuda_time_us": 15.232, + "pct_cuda_time": 0.22188413895494202, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.424, + "pct_cuda_time": 0.1664131042162065, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 159.379, + "cuda_time_us": 17.728, + "pct_cuda_time": 0.25824330458201233, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.2274778567437221, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.326, + "cuda_time_us": 3.073, + "pct_cuda_time": 0.04476430928364869, + "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": 3.073, + "pct_cuda_time": 0.04476430928364869, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 681.562, + "cuda_time_us": 135.999, + "pct_cuda_time": 1.9810938165528598, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 170.829, + "cuda_time_us": 83.231, + "pct_cuda_time": 1.2124237637446678, + "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": 83.231, + "pct_cuda_time": 1.2124237637446678, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.537, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.1305200817382012, + "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": 8.96, + "pct_cuda_time": 0.1305200817382012, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 359.714, + "cuda_time_us": 43.808, + "pct_cuda_time": 0.6381499710699908, + "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": 43.808, + "pct_cuda_time": 0.6381499710699908, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2469.643, + "cuda_time_us": 199.804, + "pct_cuda_time": 2.910539554868253, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.729, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.0438174560121104, + "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": 0.0438174560121104, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1764.788, + "cuda_time_us": 56.861999999999995, + "pct_cuda_time": 0.8283072419416958, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.436, + "cuda_time_us": 20.351, + "pct_cuda_time": 0.2964524758319344, + "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": 20.351, + "pct_cuda_time": 0.2964524758319344, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 502.881, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.054072605291540496, + "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": 0.054072605291540496, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 777.73, + "cuda_time_us": 15.008, + "pct_cuda_time": 0.21862113691148696, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.168, + "pct_cuda_time": 0.1626839590236865, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.019111869111665174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 171.888, + "cuda_time_us": 17.791, + "pct_cuda_time": 0.25916102390673407, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.679, + "pct_cuda_time": 0.2283955760684438, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.727, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.047080458055565426, + "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": 0.047080458055565426, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 472.961, + "cuda_time_us": 136.702, + "pct_cuda_time": 1.9913343988588814, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 170.117, + "cuda_time_us": 83.103, + "pct_cuda_time": 1.2105591911484077, + "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": 83.103, + "pct_cuda_time": 1.2105591911484077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.857, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.13145236803633117, + "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": 0.13145236803633117, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.333, + "cuda_time_us": 44.575, + "pct_cuda_time": 0.6493228396741426, + "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": 44.575, + "pct_cuda_time": 0.6493228396741426, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2398.708, + "cuda_time_us": 199.293, + "pct_cuda_time": 2.9030958314566218, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.042, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.043351312863045395, + "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": 0.043351312863045395, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1710.257, + "cuda_time_us": 57.951, + "pct_cuda_time": 0.8441706759833144, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.796, + "cuda_time_us": 21.184, + "pct_cuda_time": 0.30858676468103285, + "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": 21.184, + "pct_cuda_time": 0.30858676468103285, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 522.349, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.055471034738735506, + "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": 3.808, + "pct_cuda_time": 0.055471034738735506, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 725.488, + "cuda_time_us": 15.039, + "pct_cuda_time": 0.21907271308714368, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.2, + "pct_cuda_time": 0.16315010217275147, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 1.311, + "pct_cuda_time": 0.019097302138256892, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 161.192, + "cuda_time_us": 17.92, + "pct_cuda_time": 0.2610401634764024, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.808, + "pct_cuda_time": 0.2302747156381121, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.876, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.045682028608370416, + "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": 0.045682028608370416, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 461.766, + "cuda_time_us": 135.23, + "pct_cuda_time": 1.9698918140018913, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.156, + "cuda_time_us": 82.847, + "pct_cuda_time": 1.2068300459558876, + "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": 82.847, + "pct_cuda_time": 1.2068300459558876, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.803, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.1305200817382012, + "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": 8.96, + "pct_cuda_time": 0.1305200817382012, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.521, + "cuda_time_us": 43.423, + "pct_cuda_time": 0.6325416863078025, + "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": 43.423, + "pct_cuda_time": 0.6325416863078025, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2418.429, + "cuda_time_us": 200.35, + "pct_cuda_time": 2.918493122349175, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.838, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04614817175743542, + "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": 0.04614817175743542, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1744.66, + "cuda_time_us": 57.086, + "pct_cuda_time": 0.8315702439851511, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 166.091, + "cuda_time_us": 20.607, + "pct_cuda_time": 0.30018162102445445, + "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": 20.607, + "pct_cuda_time": 0.30018162102445445, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 505.88, + "cuda_time_us": 3.647, + "pct_cuda_time": 0.0531257520200022, + "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": 3.647, + "pct_cuda_time": 0.0531257520200022, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 745.006, + "cuda_time_us": 15.296, + "pct_cuda_time": 0.22281642525307202, + "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": 2.72, + "pct_cuda_time": 0.039622167670525364, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.168, + "pct_cuda_time": 0.1626839590236865, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.020510298558860187, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 176.142, + "cuda_time_us": 17.536, + "pct_cuda_time": 0.2554464456876223, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.424, + "pct_cuda_time": 0.22468099784933204, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.602, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.0456965955817787, + "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": 3.137, + "pct_cuda_time": 0.0456965955817787, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 458.527, + "cuda_time_us": 136.959, + "pct_cuda_time": 1.99507811102481, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.059, + "cuda_time_us": 84.287, + "pct_cuda_time": 1.227806487663813, + "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": 84.287, + "pct_cuda_time": 1.227806487663813, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.737, + "cuda_time_us": 9.248, + "pct_cuda_time": 0.13471537007978623, + "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": 0.13471537007978623, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 145.249, + "cuda_time_us": 43.424, + "pct_cuda_time": 0.6325562532812108, + "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": 43.424, + "pct_cuda_time": 0.6325562532812108, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2468.735, + "cuda_time_us": 199.422, + "pct_cuda_time": 2.9049749710262898, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.167, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.0438174560121104, + "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": 0.0438174560121104, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1759.998, + "cuda_time_us": 57.37599999999999, + "pct_cuda_time": 0.8357946662735525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.034, + "cuda_time_us": 20.415, + "pct_cuda_time": 0.29738476213006443, + "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": 20.415, + "pct_cuda_time": 0.29738476213006443, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 516.285, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05360646214247549, + "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": 0.05360646214247549, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 747.243, + "cuda_time_us": 15.008, + "pct_cuda_time": 0.21862113691148696, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.2, + "pct_cuda_time": 0.16315010217275147, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 186.518, + "cuda_time_us": 18.273, + "pct_cuda_time": 0.2661823050895257, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.128, + "pct_cuda_time": 0.23493614712876212, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.145, + "pct_cuda_time": 0.031246157960763563, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.306, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04614817175743542, + "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": 0.04614817175743542, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 478.235, + "cuda_time_us": 135.87, + "pct_cuda_time": 1.9792146769831913, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 166.753, + "cuda_time_us": 83.903, + "pct_cuda_time": 1.222212769875033, + "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": 83.903, + "pct_cuda_time": 1.222212769875033, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.757, + "cuda_time_us": 8.863, + "pct_cuda_time": 0.1291070853175979, + "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": 8.863, + "pct_cuda_time": 0.1291070853175979, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 152.467, + "cuda_time_us": 43.104, + "pct_cuda_time": 0.6278948217905607, + "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": 43.104, + "pct_cuda_time": 0.6278948217905607, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2363.001, + "cuda_time_us": 199.007, + "pct_cuda_time": 2.898929677061853, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.111, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1687.92, + "cuda_time_us": 56.70399999999999, + "pct_cuda_time": 0.8260056601431874, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 140.256, + "cuda_time_us": 20.576, + "pct_cuda_time": 0.2997300448487977, + "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": 20.576, + "pct_cuda_time": 0.2997300448487977, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 482.232, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05360646214247549, + "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": 0.05360646214247549, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 743.343, + "cuda_time_us": 15.008, + "pct_cuda_time": 0.21862113691148696, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.2, + "pct_cuda_time": 0.16315010217275147, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 156.936, + "cuda_time_us": 17.439999999999998, + "pct_cuda_time": 0.2540480162404273, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.328, + "pct_cuda_time": 0.22328256840213703, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.921, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04661431490650043, + "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": 3.2, + "pct_cuda_time": 0.04661431490650043, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 457.935, + "cuda_time_us": 136.06300000000002, + "pct_cuda_time": 1.9820261028509902, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 153.474, + "cuda_time_us": 83.903, + "pct_cuda_time": 1.222212769875033, + "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": 83.903, + "pct_cuda_time": 1.222212769875033, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.624, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.13145236803633117, + "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": 0.13145236803633117, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 143.349, + "cuda_time_us": 43.136, + "pct_cuda_time": 0.6283609649396257, + "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": 43.136, + "pct_cuda_time": 0.6283609649396257, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2420.999, + "cuda_time_us": 198.752, + "pct_cuda_time": 2.8952150988427414, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.258, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.043351312863045395, + "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": 0.043351312863045395, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1710.752, + "cuda_time_us": 56.801, + "pct_cuda_time": 0.8274186565637909, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 140.844, + "cuda_time_us": 20.352, + "pct_cuda_time": 0.2964670428053427, + "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": 20.352, + "pct_cuda_time": 0.2964670428053427, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 471.927, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05360646214247549, + "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": 0.05360646214247549, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 767.429, + "cuda_time_us": 15.072, + "pct_cuda_time": 0.219553423209617, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.232, + "pct_cuda_time": 0.16361624532181648, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.019111869111665174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 174.361, + "cuda_time_us": 17.697, + "pct_cuda_time": 0.2577917284063556, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.584, + "pct_cuda_time": 0.22701171359465708, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.113, + "pct_cuda_time": 0.030780014811698564, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.665, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.045682028608370416, + "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": 0.045682028608370416, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 480.866, + "cuda_time_us": 135.839, + "pct_cuda_time": 1.9787631008075346, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 172.652, + "cuda_time_us": 82.911, + "pct_cuda_time": 1.2077623322540179, + "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": 82.911, + "pct_cuda_time": 1.2077623322540179, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.925, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.1337830837816562, + "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": 0.1337830837816562, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 149.205, + "cuda_time_us": 43.744, + "pct_cuda_time": 0.6372176847718608, + "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": 43.744, + "pct_cuda_time": 0.6372176847718608, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2390.622, + "cuda_time_us": 198.684, + "pct_cuda_time": 2.8942245446509784, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.554, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1708.79, + "cuda_time_us": 56.926, + "pct_cuda_time": 0.829239528239826, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.897, + "cuda_time_us": 20.383, + "pct_cuda_time": 0.2969186189809994, + "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": 20.383, + "pct_cuda_time": 0.2969186189809994, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 531.967, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.055471034738735506, + "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": 3.808, + "pct_cuda_time": 0.055471034738735506, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 719.046, + "cuda_time_us": 14.975999999999999, + "pct_cuda_time": 0.21815499376242198, + "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": 0.03635916562707033, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.2, + "pct_cuda_time": 0.16315010217275147, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 154.214, + "cuda_time_us": 17.759, + "pct_cuda_time": 0.25869488075766905, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.615, + "pct_cuda_time": 0.2274632897703138, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.144, + "pct_cuda_time": 0.031231590987355288, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.708, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044749742310240405, + "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": 0.044749742310240405, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 457.507, + "cuda_time_us": 135.646, + "pct_cuda_time": 1.9759516749397361, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.266, + "cuda_time_us": 83.551, + "pct_cuda_time": 1.2170851952353179, + "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": 83.551, + "pct_cuda_time": 1.2170851952353179, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.679, + "cuda_time_us": 9.088, + "pct_cuda_time": 0.1323846543344612, + "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": 9.088, + "pct_cuda_time": 0.1323846543344612, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.46, + "cuda_time_us": 43.007, + "pct_cuda_time": 0.6264818253699573, + "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": 43.007, + "pct_cuda_time": 0.6264818253699573, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2706.337, + "cuda_time_us": 199.356, + "pct_cuda_time": 2.904013550781343, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 90.642, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.0438174560121104, + "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": 0.0438174560121104, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1972.876, + "cuda_time_us": 57.437, + "pct_cuda_time": 0.8366832516514577, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 163.861, + "cuda_time_us": 21.215, + "pct_cuda_time": 0.3090383408566895, + "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": 21.215, + "pct_cuda_time": 0.3090383408566895, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 496.079, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05360646214247549, + "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": 0.05360646214247549, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 980.941, + "cuda_time_us": 15.039, + "pct_cuda_time": 0.21907271308714368, + "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": 2.527, + "pct_cuda_time": 0.03681074180272706, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.2, + "pct_cuda_time": 0.16315010217275147, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.019111869111665174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 174.471, + "cuda_time_us": 17.503, + "pct_cuda_time": 0.254965735565149, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.391, + "pct_cuda_time": 0.22420028772685874, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030765447838290282, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.62, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 486.044, + "cuda_time_us": 135.87099999999998, + "pct_cuda_time": 1.9792292439565995, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 174.559, + "cuda_time_us": 83.455, + "pct_cuda_time": 1.2156867657881227, + "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": 83.455, + "pct_cuda_time": 1.2156867657881227, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.604, + "cuda_time_us": 9.088, + "pct_cuda_time": 0.1323846543344612, + "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": 9.088, + "pct_cuda_time": 0.1323846543344612, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 149.548, + "cuda_time_us": 43.328, + "pct_cuda_time": 0.6311578238340158, + "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": 43.328, + "pct_cuda_time": 0.6311578238340158, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2458.767, + "cuda_time_us": 200.99000000000004, + "pct_cuda_time": 2.9278159853304753, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.974, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.045682028608370416, + "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": 0.045682028608370416, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1766.867, + "cuda_time_us": 58.24, + "pct_cuda_time": 0.8483805312983077, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.808, + "cuda_time_us": 21.696, + "pct_cuda_time": 0.3160450550660729, + "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": 21.696, + "pct_cuda_time": 0.3160450550660729, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 526.076, + "cuda_time_us": 3.681, + "pct_cuda_time": 0.05362102911588377, + "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": 0.05362102911588377, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 777.063, + "cuda_time_us": 15.04, + "pct_cuda_time": 0.21908728006055198, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.232, + "pct_cuda_time": 0.16361624532181648, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 160.288, + "cuda_time_us": 17.823, + "pct_cuda_time": 0.2596271670557991, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.712, + "pct_cuda_time": 0.2288762861909171, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.111, + "pct_cuda_time": 0.030750880864882, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.734, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04614817175743542, + "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": 0.04614817175743542, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 468.636, + "cuda_time_us": 136.44600000000003, + "pct_cuda_time": 1.9876052536663618, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.9, + "cuda_time_us": 84.031, + "pct_cuda_time": 1.224077342471293, + "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": 1.224077342471293, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.112, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.1328507974835262, + "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": 0.1328507974835262, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 149.664, + "cuda_time_us": 43.295, + "pct_cuda_time": 0.6306771137115424, + "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": 43.295, + "pct_cuda_time": 0.6306771137115424, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2458.723, + "cuda_time_us": 199.294, + "pct_cuda_time": 2.90311039843003, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.489, + "cuda_time_us": 2.977, + "pct_cuda_time": 0.04336587983645367, + "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": 2.977, + "pct_cuda_time": 0.04336587983645367, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1776.138, + "cuda_time_us": 57.662, + "pct_cuda_time": 0.839960820668321, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 140.378, + "cuda_time_us": 20.864, + "pct_cuda_time": 0.3039253331903828, + "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": 20.864, + "pct_cuda_time": 0.3039253331903828, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 513.372, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053140318993410485, + "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": 0.053140318993410485, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 737.747, + "cuda_time_us": 15.261999999999999, + "pct_cuda_time": 0.22232114815719045, + "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": 2.527, + "pct_cuda_time": 0.03681074180272706, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.231, + "pct_cuda_time": 0.1636016783484082, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.0219087280060552, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 218.598, + "cuda_time_us": 17.887999999999998, + "pct_cuda_time": 0.26057402032733734, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.744, + "pct_cuda_time": 0.2293424293399821, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.144, + "pct_cuda_time": 0.031231590987355288, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.382, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.045682028608370416, + "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": 0.045682028608370416, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 458.882, + "cuda_time_us": 135.519, + "pct_cuda_time": 1.974101669316885, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.15, + "cuda_time_us": 83.199, + "pct_cuda_time": 1.2119576205956026, + "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": 83.199, + "pct_cuda_time": 1.2119576205956026, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.296, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.13145236803633117, + "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": 0.13145236803633117, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 145.281, + "cuda_time_us": 43.296, + "pct_cuda_time": 0.6306916806849507, + "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": 43.296, + "pct_cuda_time": 0.6306916806849507, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2487.864, + "cuda_time_us": 199.294, + "pct_cuda_time": 2.90311039843003, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.098, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1771.315, + "cuda_time_us": 57.025000000000006, + "pct_cuda_time": 0.830681658607246, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 140.46, + "cuda_time_us": 20.704, + "pct_cuda_time": 0.3015946174450578, + "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": 20.704, + "pct_cuda_time": 0.3015946174450578, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 485.487, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.054072605291540496, + "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": 0.054072605291540496, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 792.687, + "cuda_time_us": 15.04, + "pct_cuda_time": 0.21908728006055198, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.232, + "pct_cuda_time": 0.16361624532181648, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.018645725962600168, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 170.994, + "cuda_time_us": 17.569, + "pct_cuda_time": 0.25592715581009556, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.456, + "pct_cuda_time": 0.22514714099839706, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.113, + "pct_cuda_time": 0.030780014811698564, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.994, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04661431490650043, + "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": 3.2, + "pct_cuda_time": 0.04661431490650043, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 486.193, + "cuda_time_us": 136.029, + "pct_cuda_time": 1.981530825755108, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 172.044, + "cuda_time_us": 82.751, + "pct_cuda_time": 1.2054316165086927, + "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": 82.751, + "pct_cuda_time": 1.2054316165086927, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.417, + "cuda_time_us": 9.055, + "pct_cuda_time": 0.1319039442119879, + "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": 9.055, + "pct_cuda_time": 0.1319039442119879, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.636, + "cuda_time_us": 44.223, + "pct_cuda_time": 0.6441952650344276, + "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": 44.223, + "pct_cuda_time": 0.6441952650344276, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2485.63, + "cuda_time_us": 197.278, + "pct_cuda_time": 2.8737433800389347, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.711, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1775.014, + "cuda_time_us": 56.736000000000004, + "pct_cuda_time": 0.8264718032922527, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.863, + "cuda_time_us": 20.32, + "pct_cuda_time": 0.2960008996562777, + "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": 20.32, + "pct_cuda_time": 0.2960008996562777, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 527.937, + "cuda_time_us": 3.713, + "pct_cuda_time": 0.054087172264948774, + "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": 0.054087172264948774, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 753.173, + "cuda_time_us": 15.232, + "pct_cuda_time": 0.22188413895494202, + "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": 0.03682530877613533, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 11.2, + "pct_cuda_time": 0.16315010217275147, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 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": 0.0219087280060552, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 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[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 192.681, + "cuda_time_us": 17.471, + "pct_cuda_time": 0.25449959241608405, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.36, + "pct_cuda_time": 0.22374871155120205, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.111, + "pct_cuda_time": 0.030750880864882, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.743, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 477.457, + "cuda_time_us": 134.462, + "pct_cuda_time": 1.9587043784243312, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 170.141, + "cuda_time_us": 82.59, + "pct_cuda_time": 1.2030863337899595, + "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": 82.59, + "pct_cuda_time": 1.2030863337899595, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.041, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.1300539385891362, + "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": 0.1300539385891362, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 149.205, + "cuda_time_us": 42.944, + "pct_cuda_time": 0.6255641060452357, + "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": 42.944, + "pct_cuda_time": 0.6255641060452357, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.31, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0442835991611754, + "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": 0.0442835991611754, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 516.635, + "cuda_time_us": 350.619, + "pct_cuda_time": 5.107457649438211, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 4.768, + "pct_cuda_time": 0.06945532921068563, + "trace": "index_select(bfloat16[6, 4096], 0, int64[6])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.011187435577560101, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[6, 4096], bfloat16[128256, 4096], None)" + }, + "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": 345.083, + "pct_cuda_time": 5.026814884649965, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[6, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 3902.634, + "cuda_time_us": 124.92699999999999, + "pct_cuda_time": 1.819808286976368, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.824, + "pct_cuda_time": 0.026570159496705242, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.856, + "pct_cuda_time": 0.027036302645770248, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.888, + "pct_cuda_time": 0.02750244579483525, + "trace": "copy_(int32[6], int32[6], True) <- _to_copy(int32[6], 3, 0, None, None, True, None) <- to(int32[6], 3, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.856, + "pct_cuda_time": 0.027036302645770248, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.952, + "pct_cuda_time": 0.02843473209296526, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.888, + "pct_cuda_time": 0.02750244579483525, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.92, + "pct_cuda_time": 0.027968588943900256, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 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": 4.416, + "pct_cuda_time": 0.06432775457097059, + "trace": "copy_(float32[6, 128256], bfloat16[6, 128256], False) <- _to_copy(bfloat16[6, 128256], 6, None, None, None, False, None) <- to(bfloat16[6, 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": 5.504, + "pct_cuda_time": 0.08017662163918073, + "trace": "div_(float32[6, 128256], bfloat16[6, 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": 34.943, + "pct_cuda_time": 0.5090137518055763, + "trace": "_softmax(float32[6, 128256], -1, False) <- softmax(float32[6, 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": 28.384, + "pct_cuda_time": 0.41346897322065873, + "trace": "_log_softmax(float32[6, 128256], -1, False) <- log_softmax(float32[6, 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": 1.824, + "pct_cuda_time": 0.026570159496705242, + "trace": "copy_(int64[6], int32[6], False) <- _to_copy(int32[6], 4, None, None, None, False, None) <- to(int32[6], 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": 6.208, + "pct_cuda_time": 0.09043177091861082, + "trace": "index(float32[6, 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": 27.776, + "pct_cuda_time": 0.40461225338842366, + "trace": "argmax(float32[6, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 2.688, + "pct_cuda_time": 0.03915602452146036, + "trace": "copy_(int64[6], int64[6], False) <- _to_copy(int64[6], 4, 0, None, None, False, None) <- to(int64[6], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + } +} \ No newline at end of file