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"pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 419.451, + "pct_cuda_time": 0.3071768707682267, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 936.174, + "cuda_time_us": 80.768, + "pct_cuda_time": 0.05914889104617257, + "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": 80.768, + "pct_cuda_time": 0.05914889104617257, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1006.766, + "cuda_time_us": 118.527, + "pct_cuda_time": 0.08680096831702774, + "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": 33.312, + "pct_cuda_time": 0.024395402368884964, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 83.775, + "pct_cuda_time": 0.061351009649775995, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 0.0010545562983667852, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 317.468, + "cuda_time_us": 274.43, + "pct_cuda_time": 0.20097353122277561, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0005397277721502227, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 273.693, + "pct_cuda_time": 0.2004338034506254, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 137.809, + "cuda_time_us": 64.608, + "pct_cuda_time": 0.047314425920056434, + "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": 64.608, + "pct_cuda_time": 0.047314425920056434, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 609.483, + "cuda_time_us": 3015.254, + "pct_cuda_time": 2.208163261719196, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 202.228, + "cuda_time_us": 1875.9099999999999, + "pct_cuda_time": 1.3737866011591915, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0005382631106247133, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 1875.175, + "pct_cuda_time": 1.373248338048567, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 141.654, + "cuda_time_us": 258.812, + "pct_cuda_time": 0.18953598937007252, + "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": 258.812, + "pct_cuda_time": 0.18953598937007252, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 182.027, + "cuda_time_us": 880.532, + "pct_cuda_time": 0.644840671189932, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 879.796, + "pct_cuda_time": 0.6443016757485446, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2688.354, + "cuda_time_us": 4028.3329999999996, + "pct_cuda_time": 2.9500721785199766, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.334, + "cuda_time_us": 66.303, + "pct_cuda_time": 0.04855572656292566, + "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": 66.303, + "pct_cuda_time": 0.04855572656292566, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1937.129, + "cuda_time_us": 883.444, + "pct_cuda_time": 0.6469732183710737, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 168.75, + "cuda_time_us": 415.131, + "pct_cuda_time": 0.3040132018731263, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 414.395, + "pct_cuda_time": 0.30347420643173884, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 564.26, + "cuda_time_us": 80.703, + "pct_cuda_time": 0.059101289546593515, + "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": 80.703, + "pct_cuda_time": 0.059101289546593515, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 818.153, + "cuda_time_us": 117.182, + "pct_cuda_time": 0.08581598344112265, + "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": 32.767, + "pct_cuda_time": 0.02399628210318365, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 82.943, + "pct_cuda_time": 0.06074171045516408, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 0.001077990882774936, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 222.145, + "cuda_time_us": 270.428, + "pct_cuda_time": 0.1980427435102312, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 269.692, + "pct_cuda_time": 0.19750374806884377, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.493, + "cuda_time_us": 63.936, + "pct_cuda_time": 0.046822299647485265, + "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": 63.936, + "pct_cuda_time": 0.046822299647485265, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 488.556, + "cuda_time_us": 3014.6499999999996, + "pct_cuda_time": 2.207720933938492, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 171.355, + "cuda_time_us": 1875.817, + "pct_cuda_time": 1.3737184943982554, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.313, + "pct_cuda_time": 0.0009615502914969368, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 1874.504, + "pct_cuda_time": 1.3727569441067584, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.19, + "cuda_time_us": 258.941, + "pct_cuda_time": 0.18963046003846784, + "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": 258.941, + "pct_cuda_time": 0.18963046003846784, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 156.211, + "cuda_time_us": 879.8919999999999, + "pct_cuda_time": 0.6443719795017689, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.664, + "pct_cuda_time": 0.0012185983892238406, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 878.228, + "pct_cuda_time": 0.6431533811125452, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2484.7, + "cuda_time_us": 4024.1079999999997, + "pct_cuda_time": 2.946978081047338, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.376, + "cuda_time_us": 65.375, + "pct_cuda_time": 0.04787612361508929, + "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": 65.375, + "pct_cuda_time": 0.04787612361508929, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1760.72, + "cuda_time_us": 882.6129999999999, + "pct_cuda_time": 0.6463646515072246, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 154.576, + "cuda_time_us": 414.683, + "pct_cuda_time": 0.3036851176914122, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 413.947, + "pct_cuda_time": 0.30314612225002474, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 510.837, + "cuda_time_us": 80.031, + "pct_cuda_time": 0.05860916327402236, + "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": 80.031, + "pct_cuda_time": 0.05860916327402236, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 734.748, + "cuda_time_us": 117.66199999999999, + "pct_cuda_time": 0.08616750220724491, + "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": 34.015, + "pct_cuda_time": 0.02491023089510153, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 82.175, + "pct_cuda_time": 0.060179280429368445, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 0.001077990882774936, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 201.219, + "cuda_time_us": 270.23699999999997, + "pct_cuda_time": 0.1979028683345451, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 269.501, + "pct_cuda_time": 0.1973638728931576, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.918, + "cuda_time_us": 64.416, + "pct_cuda_time": 0.04717381841360752, + "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": 64.416, + "pct_cuda_time": 0.04717381841360752, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 492.362, + "cuda_time_us": 3011.7039999999997, + "pct_cuda_time": 2.2055634875114167, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 175.732, + "cuda_time_us": 1875.8480000000002, + "pct_cuda_time": 1.373741196651901, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.313, + "pct_cuda_time": 0.0009615502914969368, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 1874.535, + "pct_cuda_time": 1.3727796463604038, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.625, + "cuda_time_us": 257.884, + "pct_cuda_time": 0.18885638642223615, + "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": 257.884, + "pct_cuda_time": 0.18885638642223615, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 158.82, + "cuda_time_us": 877.972, + "pct_cuda_time": 0.64296590443728, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 877.236, + "pct_cuda_time": 0.6424269089958925, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2568.204, + "cuda_time_us": 4025.354, + "pct_cuda_time": 2.9478905651777305, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.213, + "cuda_time_us": 66.271, + "pct_cuda_time": 0.048532291978517515, + "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": 66.271, + "pct_cuda_time": 0.048532291978517515, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1850.252, + "cuda_time_us": 880.917, + "pct_cuda_time": 0.6451226185335927, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 153.242, + "cuda_time_us": 414.10699999999997, + "pct_cuda_time": 0.30326329517206546, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.376, + "pct_cuda_time": 0.0010076871295504836, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 412.731, + "pct_cuda_time": 0.302255608042515, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 536.41, + "cuda_time_us": 80.831, + "pct_cuda_time": 0.05919502788422612, + "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": 80.831, + "pct_cuda_time": 0.05919502788422612, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 759.286, + "cuda_time_us": 116.671, + "pct_cuda_time": 0.085441762421355, + "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": 32.96, + "pct_cuda_time": 0.024137621940395305, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 82.047, + "pct_cuda_time": 0.06008554209173585, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 0.0012185983892238406, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 213.56, + "cuda_time_us": 269.308, + "pct_cuda_time": 0.19722253305594595, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.0009139487919178805, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 268.06, + "pct_cuda_time": 0.19630858426402809, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.498, + "cuda_time_us": 63.712, + "pct_cuda_time": 0.04665825755662821, + "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": 63.712, + "pct_cuda_time": 0.04665825755662821, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 485.021, + "cuda_time_us": 3014.4539999999997, + "pct_cuda_time": 2.207577397108992, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 180.687, + "cuda_time_us": 1876.454, + "pct_cuda_time": 1.3741849890941302, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.632, + "pct_cuda_time": 0.0011951638048156897, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 1874.822, + "pct_cuda_time": 1.3729898252893145, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.786, + "cuda_time_us": 258.748, + "pct_cuda_time": 0.1894891202012562, + "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": 258.748, + "pct_cuda_time": 0.1894891202012562, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 153.621, + "cuda_time_us": 879.2520000000001, + "pct_cuda_time": 0.6439032878136061, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.344, + "pct_cuda_time": 0.0009842525451423329, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 877.908, + "pct_cuda_time": 0.6429190352684636, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2471.214, + "cuda_time_us": 4034.3419999999996, + "pct_cuda_time": 2.9544727540733695, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.55, + "cuda_time_us": 65.822, + "pct_cuda_time": 0.048203475466040646, + "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": 65.822, + "pct_cuda_time": 0.048203475466040646, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1749.702, + "cuda_time_us": 883.0269999999999, + "pct_cuda_time": 0.646667836443005, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.41, + "cuda_time_us": 415.29, + "pct_cuda_time": 0.3041296424644043, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.663, + "pct_cuda_time": 0.001217866058461086, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 413.627, + "pct_cuda_time": 0.30291177640594324, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 495.662, + "cuda_time_us": 80.287, + "pct_cuda_time": 0.05879663994928756, + "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": 80.287, + "pct_cuda_time": 0.05879663994928756, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 723.391, + "cuda_time_us": 116.478, + "pct_cuda_time": 0.08530042258414333, + "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": 32.992, + "pct_cuda_time": 0.024161056524803454, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 81.758, + "pct_cuda_time": 0.05987389850129973, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.728, + "pct_cuda_time": 0.0012654675580401422, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 224.876, + "cuda_time_us": 270.972, + "pct_cuda_time": 0.19844113144516978, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.311, + "pct_cuda_time": 0.0009600856299714273, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 269.661, + "pct_cuda_time": 0.19748104581519837, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.718, + "cuda_time_us": 64.319, + "pct_cuda_time": 0.04710278232962032, + "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": 64.319, + "pct_cuda_time": 0.04710278232962032, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 489.646, + "cuda_time_us": 3021.174, + "pct_cuda_time": 2.2124986598347043, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 170.039, + "cuda_time_us": 1882.695, + "pct_cuda_time": 1.3787554653844822, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.408, + "pct_cuda_time": 0.0010311217139586343, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 1881.287, + "pct_cuda_time": 1.3777243436705238, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.705, + "cuda_time_us": 258.268, + "pct_cuda_time": 0.18913760143513392, + "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": 258.268, + "pct_cuda_time": 0.18913760143513392, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 156.035, + "cuda_time_us": 880.211, + "pct_cuda_time": 0.6446055930150877, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.767, + "pct_cuda_time": 0.000561697695032864, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 879.444, + "pct_cuda_time": 0.6440438953200549, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2544.573, + "cuda_time_us": 4032.9399999999996, + "pct_cuda_time": 2.9534460263439875, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.926, + "cuda_time_us": 66.879, + "pct_cuda_time": 0.048977549082272386, + "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": 66.879, + "pct_cuda_time": 0.048977549082272386, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1791.248, + "cuda_time_us": 882.8389999999999, + "pct_cuda_time": 0.6465301582596071, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 149.332, + "cuda_time_us": 414.33099999999996, + "pct_cuda_time": 0.30342733726292254, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0009373833763260313, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 413.051, + "pct_cuda_time": 0.3024899538865965, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 524.134, + "cuda_time_us": 80.287, + "pct_cuda_time": 0.05879663994928756, + "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": 80.287, + "pct_cuda_time": 0.05879663994928756, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 736.0, + "cuda_time_us": 116.79899999999999, + "pct_cuda_time": 0.0855355007589876, + "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": 32.928, + "pct_cuda_time": 0.02411418735598715, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 82.463, + "pct_cuda_time": 0.060390191689041806, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 0.0010311217139586343, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 227.45, + "cuda_time_us": 271.422, + "pct_cuda_time": 0.19877068028840944, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.793, + "pct_cuda_time": 0.0013130690576191985, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 269.629, + "pct_cuda_time": 0.19745761123079023, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 91.085, + "cuda_time_us": 63.839, + "pct_cuda_time": 0.046751263563498055, + "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": 63.839, + "pct_cuda_time": 0.046751263563498055, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 503.62, + "cuda_time_us": 3019.383, + "pct_cuda_time": 2.2111870554386104, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 173.943, + "cuda_time_us": 1880.519, + "pct_cuda_time": 1.3771619136447282, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.952, + "pct_cuda_time": 0.0014295096488971977, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 1878.567, + "pct_cuda_time": 1.375732403995831, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.181, + "cuda_time_us": 258.844, + "pct_cuda_time": 0.18955942395448067, + "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": 258.844, + "pct_cuda_time": 0.18955942395448067, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 158.041, + "cuda_time_us": 880.02, + "pct_cuda_time": 0.6444657178394017, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.632, + "pct_cuda_time": 0.0011951638048156897, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 878.388, + "pct_cuda_time": 0.643270554034586, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2505.324, + "cuda_time_us": 4033.6099999999997, + "pct_cuda_time": 2.953936687955033, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.371, + "cuda_time_us": 65.534, + "pct_cuda_time": 0.0479925642063673, + "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": 65.534, + "pct_cuda_time": 0.0479925642063673, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1796.798, + "cuda_time_us": 884.149, + "pct_cuda_time": 0.6474895115588157, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.464, + "cuda_time_us": 415.034, + "pct_cuda_time": 0.30394216578913913, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0005624300257956187, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 414.266, + "pct_cuda_time": 0.3033797357633435, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 554.119, + "cuda_time_us": 80.575, + "pct_cuda_time": 0.05900755120896091, + "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": 80.575, + "pct_cuda_time": 0.05900755120896091, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 726.953, + "cuda_time_us": 117.535, + "pct_cuda_time": 0.08607449620037506, + "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": 34.015, + "pct_cuda_time": 0.02491023089510153, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 82.048, + "pct_cuda_time": 0.06008627442249861, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 0.001077990882774936, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 204.941, + "cuda_time_us": 271.005, + "pct_cuda_time": 0.19846529836034071, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 270.269, + "pct_cuda_time": 0.19792630291895325, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.073, + "cuda_time_us": 65.279, + "pct_cuda_time": 0.04780581986186484, + "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": 65.279, + "pct_cuda_time": 0.04780581986186484, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 476.23, + "cuda_time_us": 3018.6479999999997, + "pct_cuda_time": 2.2106487923279854, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 169.683, + "cuda_time_us": 1880.4869999999999, + "pct_cuda_time": 1.3771384790603198, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.000960817960734182, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 1879.175, + "pct_cuda_time": 1.3761776610995857, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.073, + "cuda_time_us": 257.565, + "pct_cuda_time": 0.18862277290891738, + "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": 257.565, + "pct_cuda_time": 0.18862277290891738, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 152.774, + "cuda_time_us": 880.596, + "pct_cuda_time": 0.6448875403587483, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0005624300257956187, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 879.828, + "pct_cuda_time": 0.6443251103329527, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2531.921, + "cuda_time_us": 4073.033, + "pct_cuda_time": 2.9828073636151125, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.406, + "cuda_time_us": 66.783, + "pct_cuda_time": 0.04890724532904793, + "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": 66.783, + "pct_cuda_time": 0.04890724532904793, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1771.43, + "cuda_time_us": 889.6199999999999, + "pct_cuda_time": 0.6514960931618468, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 153.583, + "cuda_time_us": 415.22499999999997, + "pct_cuda_time": 0.3040820409648253, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.503, + "pct_cuda_time": 0.0011006931364203319, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 413.722, + "pct_cuda_time": 0.3029813478284049, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 505.338, + "cuda_time_us": 81.247, + "pct_cuda_time": 0.05949967748153208, + "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": 81.247, + "pct_cuda_time": 0.05949967748153208, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 737.048, + "cuda_time_us": 119.583, + "pct_cuda_time": 0.08757430960249672, + "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": 33.344, + "pct_cuda_time": 0.024418836953293117, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 84.543, + "pct_cuda_time": 0.06191343967557162, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.696, + "pct_cuda_time": 0.0012420329736319913, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 225.763, + "cuda_time_us": 273.565, + "pct_cuda_time": 0.20034006511299277, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 272.829, + "pct_cuda_time": 0.1998010696716053, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 88.163, + "cuda_time_us": 64.927, + "pct_cuda_time": 0.04754803943337519, + "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": 64.927, + "pct_cuda_time": 0.04754803943337519, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 520.977, + "cuda_time_us": 3051.703, + "pct_cuda_time": 2.2348559856908428, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 206.129, + "cuda_time_us": 1902.3419999999999, + "pct_cuda_time": 1.3931435678803241, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.696, + "pct_cuda_time": 0.0012420329736319913, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 1900.646, + "pct_cuda_time": 1.391901534906692, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.666, + "cuda_time_us": 261.597, + "pct_cuda_time": 0.19157553054434437, + "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": 261.597, + "pct_cuda_time": 0.19157553054434437, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 159.057, + "cuda_time_us": 887.764, + "pct_cuda_time": 0.6501368872661741, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.000960817960734182, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 886.452, + "pct_cuda_time": 0.6491760693054399, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2478.292, + "cuda_time_us": 4099.593999999999, + "pct_cuda_time": 3.00225880100464, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.16, + "cuda_time_us": 66.559, + "pct_cuda_time": 0.04874320323819087, + "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": 66.559, + "pct_cuda_time": 0.04874320323819087, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1760.583, + "cuda_time_us": 905.4279999999999, + "pct_cuda_time": 0.6630727778594733, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 163.961, + "cuda_time_us": 427.64300000000003, + "pct_cuda_time": 0.3131761243767133, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.0011014254671830868, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 426.139, + "pct_cuda_time": 0.3120746989095302, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 500.65, + "cuda_time_us": 81.918, + "pct_cuda_time": 0.0599910714233405, + "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": 81.918, + "pct_cuda_time": 0.0599910714233405, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 725.683, + "cuda_time_us": 120.767, + "pct_cuda_time": 0.08844138922559829, + "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": 33.536, + "pct_cuda_time": 0.024559444459742022, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 85.567, + "pct_cuda_time": 0.06266334637663243, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 0.0012185983892238406, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 203.713, + "cuda_time_us": 275.09999999999997, + "pct_cuda_time": 0.20146419283382125, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0009373833763260313, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 273.82, + "pct_cuda_time": 0.2005268094574952, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.541, + "cuda_time_us": 65.151, + "pct_cuda_time": 0.04771208152423224, + "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": 65.151, + "pct_cuda_time": 0.04771208152423224, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 483.263, + "cuda_time_us": 3062.4559999999997, + "pct_cuda_time": 2.2427307383827437, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.924, + "cuda_time_us": 1914.663, + "pct_cuda_time": 1.402166615208225, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 1913.927, + "pct_cuda_time": 1.4016276197668374, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.374, + "cuda_time_us": 261.693, + "pct_cuda_time": 0.19164583429756882, + "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": 261.693, + "pct_cuda_time": 0.19164583429756882, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 165.908, + "cuda_time_us": 886.1, + "pct_cuda_time": 0.6489182888769502, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 885.364, + "pct_cuda_time": 0.6483792934355628, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2398.241, + "cuda_time_us": 4104.618, + "pct_cuda_time": 3.0059380307567203, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.011, + "cuda_time_us": 67.007, + "pct_cuda_time": 0.04907128741990499, + "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": 67.007, + "pct_cuda_time": 0.04907128741990499, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1708.273, + "cuda_time_us": 905.909, + "pct_cuda_time": 0.6634250289563584, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.045, + "cuda_time_us": 427.483, + "pct_cuda_time": 0.3130589514546725, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.6, + "pct_cuda_time": 0.001171729220407539, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 425.883, + "pct_cuda_time": 0.31188722223426496, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 480.079, + "cuda_time_us": 82.111, + "pct_cuda_time": 0.06013241126055216, + "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": 82.111, + "pct_cuda_time": 0.06013241126055216, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 735.092, + "cuda_time_us": 120.959, + "pct_cuda_time": 0.0885819967320472, + "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": 34.432, + "pct_cuda_time": 0.02521561282317024, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 85.023, + "pct_cuda_time": 0.062264958441693864, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 0.0011014254671830868, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 198.278, + "cuda_time_us": 275.356, + "pct_cuda_time": 0.20165166950908645, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.664, + "pct_cuda_time": 0.0012185983892238406, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 273.692, + "pct_cuda_time": 0.20043307111986264, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.111, + "cuda_time_us": 64.543, + "pct_cuda_time": 0.04726682442047738, + "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": 64.543, + "pct_cuda_time": 0.04726682442047738, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 464.999, + "cuda_time_us": 3067.159, + "pct_cuda_time": 2.2461748899599794, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.727, + "cuda_time_us": 1916.295, + "pct_cuda_time": 1.4033617790130408, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 1915.559, + "pct_cuda_time": 1.4028227835716531, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.931, + "cuda_time_us": 262.332, + "pct_cuda_time": 0.19211379365496908, + "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": 262.332, + "pct_cuda_time": 0.19211379365496908, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.13, + "cuda_time_us": 888.532, + "pct_cuda_time": 0.6506993172919697, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.0011014254671830868, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 887.028, + "pct_cuda_time": 0.6495978918247867, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2492.546, + "cuda_time_us": 4083.0520000000006, + "pct_cuda_time": 2.9901445855271525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.929, + "cuda_time_us": 65.791, + "pct_cuda_time": 0.04818077321239525, + "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": 65.791, + "pct_cuda_time": 0.04818077321239525, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1741.696, + "cuda_time_us": 905.173, + "pct_cuda_time": 0.6628860335149709, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.575, + "cuda_time_us": 426.651, + "pct_cuda_time": 0.3124496522600606, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0005397277721502227, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 425.914, + "pct_cuda_time": 0.3119099244879104, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 491.332, + "cuda_time_us": 81.376, + "pct_cuda_time": 0.05959414814992744, + "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": 81.376, + "pct_cuda_time": 0.05959414814992744, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 730.22, + "cuda_time_us": 121.886, + "pct_cuda_time": 0.08926086734912081, + "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": 34.303, + "pct_cuda_time": 0.02512114215477488, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 86.111, + "pct_cuda_time": 0.063061734311571, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 0.001077990882774936, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 221.37, + "cuda_time_us": 275.26, + "pct_cuda_time": 0.201581365755862, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0009373833763260313, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 273.98, + "pct_cuda_time": 0.20064398237953596, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.423, + "cuda_time_us": 64.639, + "pct_cuda_time": 0.04733712817370182, + "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": 64.639, + "pct_cuda_time": 0.04733712817370182, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 518.787, + "cuda_time_us": 3047.4490000000005, + "pct_cuda_time": 2.2317406506260844, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 178.346, + "cuda_time_us": 1900.583, + "pct_cuda_time": 1.3918553980686386, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 1899.847, + "pct_cuda_time": 1.3913164026272513, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.685, + "cuda_time_us": 261.981, + "pct_cuda_time": 0.19185674555724216, + "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": 261.981, + "pct_cuda_time": 0.19185674555724216, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 164.241, + "cuda_time_us": 884.885, + "pct_cuda_time": 0.6480285070002032, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0005397277721502227, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 884.148, + "pct_cuda_time": 0.647488779228053, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2551.603, + "cuda_time_us": 4096.908, + "pct_cuda_time": 3.0002917605758816, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 134.685, + "cuda_time_us": 65.568, + "pct_cuda_time": 0.04801746345230095, + "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": 65.568, + "pct_cuda_time": 0.04801746345230095, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1780.116, + "cuda_time_us": 905.046, + "pct_cuda_time": 0.6627930275081011, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 160.766, + "cuda_time_us": 428.66700000000003, + "pct_cuda_time": 0.3139260310777741, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.473, + "pct_cuda_time": 0.0010787232135376907, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 427.194, + "pct_cuda_time": 0.3128473078642364, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 489.741, + "cuda_time_us": 81.983, + "pct_cuda_time": 0.060038672922919555, + "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": 81.983, + "pct_cuda_time": 0.060038672922919555, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 730.02, + "cuda_time_us": 120.89499999999998, + "pct_cuda_time": 0.08853512756323088, + "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": 33.504, + "pct_cuda_time": 0.024536009875333865, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 85.695, + "pct_cuda_time": 0.06275708471426503, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.696, + "pct_cuda_time": 0.0012420329736319913, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 207.887, + "cuda_time_us": 273.501, + "pct_cuda_time": 0.20029319594417647, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 272.765, + "pct_cuda_time": 0.19975420050278897, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.066, + "cuda_time_us": 63.711, + "pct_cuda_time": 0.04665752522586545, + "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": 63.711, + "pct_cuda_time": 0.04665752522586545, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 467.13, + "cuda_time_us": 3062.583, + "pct_cuda_time": 2.242823744389614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 166.061, + "cuda_time_us": 1908.486, + "pct_cuda_time": 1.3976430080866893, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.536, + "pct_cuda_time": 0.0011248600515912375, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 1906.95, + "pct_cuda_time": 1.3965181480350979, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.412, + "cuda_time_us": 262.269, + "pct_cuda_time": 0.19206765681691554, + "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": 262.269, + "pct_cuda_time": 0.19206765681691554, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.494, + "cuda_time_us": 891.828, + "pct_cuda_time": 0.6531130794860093, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.216, + "pct_cuda_time": 0.0008905142075097296, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 890.612, + "pct_cuda_time": 0.6522225652784995, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2590.148, + "cuda_time_us": 4332.934, + "pct_cuda_time": 3.1731408611858254, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.529, + "cuda_time_us": 65.983, + "pct_cuda_time": 0.04832138071884416, + "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": 65.983, + "pct_cuda_time": 0.04832138071884416, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1854.98, + "cuda_time_us": 959.923, + "pct_cuda_time": 0.7029811427757914, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.828, + "cuda_time_us": 457.978, + "pct_cuda_time": 0.3353913780648775, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.6, + "pct_cuda_time": 0.001171729220407539, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 456.378, + "pct_cuda_time": 0.3342196488444699, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 497.174, + "cuda_time_us": 84.127, + "pct_cuda_time": 0.06160879007826565, + "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": 84.127, + "pct_cuda_time": 0.06160879007826565, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 813.361, + "cuda_time_us": 127.96600000000001, + "pct_cuda_time": 0.09371343838666947, + "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": 34.463, + "pct_cuda_time": 0.025238315076815638, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 91.711, + "pct_cuda_time": 0.06716278658299739, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.792, + "pct_cuda_time": 0.0013123367268564436, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 233.781, + "cuda_time_us": 289.852, + "pct_cuda_time": 0.21226753624597874, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0009373833763260313, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 288.572, + "pct_cuda_time": 0.21133015286965273, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 92.085, + "cuda_time_us": 65.183, + "pct_cuda_time": 0.047735516108640394, + "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": 65.183, + "pct_cuda_time": 0.047735516108640394, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 495.812, + "cuda_time_us": 3241.8450000000003, + "pct_cuda_time": 2.3741028215825493, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 176.333, + "cuda_time_us": 2054.469, + "pct_cuda_time": 1.5045508498259104, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0005624300257956187, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2053.701, + "pct_cuda_time": 1.5039884198001148, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.45, + "cuda_time_us": 267.612, + "pct_cuda_time": 0.195980500082314, + "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": 267.612, + "pct_cuda_time": 0.195980500082314, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 161.889, + "cuda_time_us": 919.764, + "pct_cuda_time": 0.6735714716743249, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 919.028, + "pct_cuda_time": 0.6730324762329374, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2446.162, + "cuda_time_us": 4337.094, + "pct_cuda_time": 3.176187357158885, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.916, + "cuda_time_us": 66.527, + "pct_cuda_time": 0.04871976865378272, + "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": 66.527, + "pct_cuda_time": 0.04871976865378272, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1742.305, + "cuda_time_us": 960.4670000000001, + "pct_cuda_time": 0.7033795307107299, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 157.426, + "cuda_time_us": 459.098, + "pct_cuda_time": 0.3362115885191628, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.632, + "pct_cuda_time": 0.0011951638048156897, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 457.466, + "pct_cuda_time": 0.33501642471434706, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 515.075, + "cuda_time_us": 84.511, + "pct_cuda_time": 0.06189000509116345, + "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": 84.511, + "pct_cuda_time": 0.06189000509116345, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 718.87, + "cuda_time_us": 126.46199999999999, + "pct_cuda_time": 0.09261201291948637, + "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": 33.983, + "pct_cuda_time": 0.024886796310693373, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 90.943, + "pct_cuda_time": 0.06660035655720177, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.536, + "pct_cuda_time": 0.0011248600515912375, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 201.71, + "cuda_time_us": 290.396, + "pct_cuda_time": 0.21266592418091734, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.6, + "pct_cuda_time": 0.001171729220407539, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 288.796, + "pct_cuda_time": 0.21149419496050975, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.865, + "cuda_time_us": 64.703, + "pct_cuda_time": 0.04738399734251813, + "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": 64.703, + "pct_cuda_time": 0.04738399734251813, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.341, + "cuda_time_us": 3245.397, + "pct_cuda_time": 2.376704060451854, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 170.283, + "cuda_time_us": 2060.101, + "pct_cuda_time": 1.508675336681745, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.000960817960734182, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2058.789, + "pct_cuda_time": 1.5077145187210106, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.334, + "cuda_time_us": 267.58, + "pct_cuda_time": 0.19595706549790579, + "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": 267.58, + "pct_cuda_time": 0.19595706549790579, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 150.58, + "cuda_time_us": 917.716, + "pct_cuda_time": 0.6720716582722032, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.568, + "pct_cuda_time": 0.0011482946359993884, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 916.148, + "pct_cuda_time": 0.6709233636362039, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2391.831, + "cuda_time_us": 4329.03, + "pct_cuda_time": 3.1702818418880305, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.145, + "cuda_time_us": 67.455, + "pct_cuda_time": 0.0493993716016191, + "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": 67.455, + "pct_cuda_time": 0.0493993716016191, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1700.451, + "cuda_time_us": 958.9309999999999, + "pct_cuda_time": 0.7022546706591386, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.063, + "cuda_time_us": 457.082, + "pct_cuda_time": 0.33473520970144927, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0005624300257956187, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 456.314, + "pct_cuda_time": 0.3341727796756536, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 478.593, + "cuda_time_us": 84.895, + "pct_cuda_time": 0.06217122010406126, + "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": 84.895, + "pct_cuda_time": 0.06217122010406126, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 706.392, + "cuda_time_us": 126.62199999999999, + "pct_cuda_time": 0.09272918584152713, + "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": 34.335, + "pct_cuda_time": 0.025144576739183036, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 90.591, + "pct_cuda_time": 0.06634257612871211, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.696, + "pct_cuda_time": 0.0012420329736319913, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 200.396, + "cuda_time_us": 290.332, + "pct_cuda_time": 0.212619055012101, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.0009139487919178805, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 289.084, + "pct_cuda_time": 0.21170510622018313, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.461, + "cuda_time_us": 64.447, + "pct_cuda_time": 0.04719652066725292, + "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": 64.447, + "pct_cuda_time": 0.04719652066725292, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 467.109, + "cuda_time_us": 3238.1969999999997, + "pct_cuda_time": 2.3714312789600194, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.315, + "cuda_time_us": 2053.605, + "pct_cuda_time": 1.5039181160468902, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2052.869, + "pct_cuda_time": 1.503379120605503, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.08, + "cuda_time_us": 267.26, + "pct_cuda_time": 0.1957227196538243, + "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": 267.26, + "pct_cuda_time": 0.1957227196538243, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 157.171, + "cuda_time_us": 917.332, + "pct_cuda_time": 0.6717904432593054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 916.596, + "pct_cuda_time": 0.6712514478179179, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2457.687, + "cuda_time_us": 4330.918, + "pct_cuda_time": 3.171664482368111, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.556, + "cuda_time_us": 66.399, + "pct_cuda_time": 0.04862603031615012, + "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": 66.399, + "pct_cuda_time": 0.04862603031615012, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1736.883, + "cuda_time_us": 958.163, + "pct_cuda_time": 0.701692240633343, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.965, + "cuda_time_us": 458.106, + "pct_cuda_time": 0.33548511640251005, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.0011014254671830868, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 456.602, + "pct_cuda_time": 0.33438369093532694, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 505.589, + "cuda_time_us": 84.991, + "pct_cuda_time": 0.06224152385728572, + "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": 84.991, + "pct_cuda_time": 0.06224152385728572, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 729.163, + "cuda_time_us": 127.13399999999999, + "pct_cuda_time": 0.09310413919205754, + "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": 34.303, + "pct_cuda_time": 0.02512114215477488, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 91.103, + "pct_cuda_time": 0.06671752947924252, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.728, + "pct_cuda_time": 0.0012654675580401422, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 196.475, + "cuda_time_us": 287.932, + "pct_cuda_time": 0.21086146118148974, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 287.196, + "pct_cuda_time": 0.21032246574010227, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 100.757, + "cuda_time_us": 65.087, + "pct_cuda_time": 0.04766521235541594, + "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": 65.087, + "pct_cuda_time": 0.04766521235541594, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 470.647, + "cuda_time_us": 3241.2690000000002, + "pct_cuda_time": 2.3736809990632026, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.111, + "cuda_time_us": 2054.085, + "pct_cuda_time": 1.5042696348130125, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.696, + "pct_cuda_time": 0.0012420329736319913, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2052.389, + "pct_cuda_time": 1.5030276018393807, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.148, + "cuda_time_us": 267.676, + "pct_cuda_time": 0.19602736925113023, + "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": 267.676, + "pct_cuda_time": 0.19602736925113023, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 153.188, + "cuda_time_us": 919.508, + "pct_cuda_time": 0.6733839949990597, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 918.772, + "pct_cuda_time": 0.6728449995576722, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2340.591, + "cuda_time_us": 4328.263000000001, + "pct_cuda_time": 3.169720144192998, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.044, + "cuda_time_us": 67.295, + "pct_cuda_time": 0.04928219867957834, + "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": 67.295, + "pct_cuda_time": 0.04928219867957834, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1665.935, + "cuda_time_us": 958.3230000000001, + "pct_cuda_time": 0.7018094135553838, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 149.14, + "cuda_time_us": 457.466, + "pct_cuda_time": 0.33501642471434706, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.0011014254671830868, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 455.962, + "pct_cuda_time": 0.33391499924716395, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 479.448, + "cuda_time_us": 84.191, + "pct_cuda_time": 0.061655659247081954, + "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": 84.191, + "pct_cuda_time": 0.061655659247081954, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 704.469, + "cuda_time_us": 127.13399999999999, + "pct_cuda_time": 0.09310413919205754, + "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": 34.335, + "pct_cuda_time": 0.025144576739183036, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 91.103, + "pct_cuda_time": 0.06671752947924252, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.696, + "pct_cuda_time": 0.0012420329736319913, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 190.915, + "cuda_time_us": 289.532, + "pct_cuda_time": 0.21203319040189725, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.0009139487919178805, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 288.284, + "pct_cuda_time": 0.21111924160997936, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.14, + "cuda_time_us": 64.767, + "pct_cuda_time": 0.04743086651133442, + "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": 64.767, + "pct_cuda_time": 0.04743086651133442, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 460.151, + "cuda_time_us": 3237.878, + "pct_cuda_time": 2.3711976654467013, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.201, + "cuda_time_us": 2053.51, + "pct_cuda_time": 1.5038485446244287, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0005397277721502227, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2052.773, + "pct_cuda_time": 1.5033088168522784, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.674, + "cuda_time_us": 267.196, + "pct_cuda_time": 0.19567585048500805, + "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": 267.196, + "pct_cuda_time": 0.19567585048500805, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 149.624, + "cuda_time_us": 917.172, + "pct_cuda_time": 0.6716732703372646, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.344, + "pct_cuda_time": 0.0009842525451423329, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 915.828, + "pct_cuda_time": 0.6706890177921223, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2436.917, + "cuda_time_us": 4334.216, + "pct_cuda_time": 3.174079709223677, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.505, + "cuda_time_us": 66.239, + "pct_cuda_time": 0.048508857394109366, + "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": 66.239, + "pct_cuda_time": 0.048508857394109366, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1740.316, + "cuda_time_us": 960.499, + "pct_cuda_time": 0.703402965295138, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 149.904, + "cuda_time_us": 458.714, + "pct_cuda_time": 0.3359303735062649, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.000960817960734182, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 457.402, + "pct_cuda_time": 0.33496955554553076, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 493.489, + "cuda_time_us": 84.639, + "pct_cuda_time": 0.061983743428796055, + "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": 84.639, + "pct_cuda_time": 0.061983743428796055, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 751.703, + "cuda_time_us": 126.718, + "pct_cuda_time": 0.09279948959475158, + "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": 34.304, + "pct_cuda_time": 0.025121874485537637, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 90.911, + "pct_cuda_time": 0.0665769219727936, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 0.0011006931364203319, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 203.396, + "cuda_time_us": 290.42800000000005, + "pct_cuda_time": 0.2126893587653255, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.696, + "pct_cuda_time": 0.0012420329736319913, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 288.732, + "pct_cuda_time": 0.2114473257916935, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.457, + "cuda_time_us": 65.088, + "pct_cuda_time": 0.047665944686178685, + "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": 65.088, + "pct_cuda_time": 0.047665944686178685, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 476.238, + "cuda_time_us": 3242.39, + "pct_cuda_time": 2.3745019418482505, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 166.204, + "cuda_time_us": 2054.5009999999997, + "pct_cuda_time": 1.5045742844103183, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.000960817960734182, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2053.189, + "pct_cuda_time": 1.5036134664495842, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.789, + "cuda_time_us": 267.324, + "pct_cuda_time": 0.19576958882264062, + "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": 267.324, + "pct_cuda_time": 0.19576958882264062, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.252, + "cuda_time_us": 920.5649999999999, + "pct_cuda_time": 0.6741580686152914, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.569, + "pct_cuda_time": 0.0011490269667621428, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 918.996, + "pct_cuda_time": 0.6730090416485293, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2355.734, + "cuda_time_us": 4343.848, + "pct_cuda_time": 3.18113351913053, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.326, + "cuda_time_us": 67.327, + "pct_cuda_time": 0.04930563326398649, + "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": 67.327, + "pct_cuda_time": 0.04930563326398649, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1672.305, + "cuda_time_us": 959.8919999999998, + "pct_cuda_time": 0.7029584405221458, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 152.608, + "cuda_time_us": 457.018, + "pct_cuda_time": 0.3346883405326329, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 456.282, + "pct_cuda_time": 0.33414934509124544, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 479.593, + "cuda_time_us": 84.255, + "pct_cuda_time": 0.061702528415898246, + "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": 84.255, + "pct_cuda_time": 0.061702528415898246, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 704.931, + "cuda_time_us": 128.41500000000002, + "pct_cuda_time": 0.09404225489914635, + "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": 35.487, + "pct_cuda_time": 0.025988221777876467, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 91.391, + "pct_cuda_time": 0.06692844073891588, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.537, + "pct_cuda_time": 0.0011255923823539921, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 190.782, + "cuda_time_us": 290.204, + "pct_cuda_time": 0.21252531667446842, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.0009139487919178805, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 288.956, + "pct_cuda_time": 0.21161136788255056, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.11, + "cuda_time_us": 64.543, + "pct_cuda_time": 0.04726682442047738, + "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": 64.543, + "pct_cuda_time": 0.04726682442047738, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 467.258, + "cuda_time_us": 3252.0860000000002, + "pct_cuda_time": 2.3816026209239203, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 167.07, + "cuda_time_us": 2059.4300000000003, + "pct_cuda_time": 1.5081839427399366, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0005397277721502227, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2058.693, + "pct_cuda_time": 1.5076442149677862, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.382, + "cuda_time_us": 267.644, + "pct_cuda_time": 0.1960039346667221, + "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": 267.644, + "pct_cuda_time": 0.1960039346667221, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 152.171, + "cuda_time_us": 925.012, + "pct_cuda_time": 0.6774147435172615, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 924.276, + "pct_cuda_time": 0.6768757480758741, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2522.872, + "cuda_time_us": 4371.014999999999, + "pct_cuda_time": 3.201028748962287, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.376, + "cuda_time_us": 66.591, + "pct_cuda_time": 0.04876663782259902, + "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": 66.591, + "pct_cuda_time": 0.04876663782259902, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1786.633, + "cuda_time_us": 966.2909999999999, + "pct_cuda_time": 0.7076446250730133, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 173.762, + "cuda_time_us": 462.586, + "pct_cuda_time": 0.33876595821965116, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.216, + "pct_cuda_time": 0.0008905142075097296, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 461.37, + "pct_cuda_time": 0.3378754440121414, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 468.173, + "cuda_time_us": 84.927, + "pct_cuda_time": 0.06219465468846943, + "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": 84.927, + "pct_cuda_time": 0.06219465468846943, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 784.39, + "cuda_time_us": 127.678, + "pct_cuda_time": 0.09350252712699611, + "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": 34.559, + "pct_cuda_time": 0.02530861883004009, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 91.359, + "pct_cuda_time": 0.06690500615450773, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.76, + "pct_cuda_time": 0.001288902142448293, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 218.189, + "cuda_time_us": 291.09999999999997, + "pct_cuda_time": 0.2131814850378966, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0005624300257956187, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 290.332, + "pct_cuda_time": 0.212619055012101, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 88.254, + "cuda_time_us": 64.863, + "pct_cuda_time": 0.04750117026455888, + "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": 64.863, + "pct_cuda_time": 0.04750117026455888, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 504.41, + "cuda_time_us": 3273.27, + "pct_cuda_time": 2.397116315802116, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 169.535, + "cuda_time_us": 2077.797, + "pct_cuda_time": 1.521634661859452, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.536, + "pct_cuda_time": 0.0011248600515912375, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2076.261, + "pct_cuda_time": 1.5205098018078609, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.453, + "cuda_time_us": 267.453, + "pct_cuda_time": 0.19586405949103594, + "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": 267.453, + "pct_cuda_time": 0.19586405949103594, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 163.296, + "cuda_time_us": 928.02, + "pct_cuda_time": 0.6796175944516277, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 927.284, + "pct_cuda_time": 0.6790785990102403, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2477.662, + "cuda_time_us": 4365.253, + "pct_cuda_time": 3.1968090591072946, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.254, + "cuda_time_us": 66.559, + "pct_cuda_time": 0.04874320323819087, + "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": 66.559, + "pct_cuda_time": 0.04874320323819087, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1772.541, + "cuda_time_us": 966.673, + "pct_cuda_time": 0.7079243754243857, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 154.34, + "cuda_time_us": 462.394, + "pct_cuda_time": 0.33862535071320227, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.824, + "pct_cuda_time": 0.0013357713112645945, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 460.57, + "pct_cuda_time": 0.33728957940193766, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 486.446, + "cuda_time_us": 85.022, + "pct_cuda_time": 0.062264226110931124, + "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": 85.022, + "pct_cuda_time": 0.062264226110931124, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 762.391, + "cuda_time_us": 128.15800000000002, + "pct_cuda_time": 0.09385404589311838, + "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": 34.752, + "pct_cuda_time": 0.025449958667251752, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 91.678, + "pct_cuda_time": 0.06713861966782647, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.728, + "pct_cuda_time": 0.0012654675580401422, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 215.89, + "cuda_time_us": 291.099, + "pct_cuda_time": 0.21318075270713388, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 290.363, + "pct_cuda_time": 0.2126417572657464, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.546, + "cuda_time_us": 64.735, + "pct_cuda_time": 0.04740743192692628, + "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": 64.735, + "pct_cuda_time": 0.04740743192692628, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 472.799, + "cuda_time_us": 3267.286, + "pct_cuda_time": 2.3927340485177915, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 169.942, + "cuda_time_us": 2074.1169999999997, + "pct_cuda_time": 1.5189396846525145, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2073.381, + "pct_cuda_time": 1.5184006892111273, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.443, + "cuda_time_us": 267.677, + "pct_cuda_time": 0.19602810158189302, + "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": 267.677, + "pct_cuda_time": 0.19602810158189302, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 150.827, + "cuda_time_us": 925.492, + "pct_cuda_time": 0.6777662622833839, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.313, + "pct_cuda_time": 0.0009615502914969368, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 924.179, + "pct_cuda_time": 0.676804711991887, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2339.089, + "cuda_time_us": 4367.558, + "pct_cuda_time": 3.198497081515444, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.858, + "cuda_time_us": 67.039, + "pct_cuda_time": 0.049094722004313134, + "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": 67.039, + "pct_cuda_time": 0.049094722004313134, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1653.419, + "cuda_time_us": 966.259, + "pct_cuda_time": 0.7076211904886052, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.07, + "cuda_time_us": 461.65799999999996, + "pct_cuda_time": 0.3380863552718148, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0009373833763260313, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 460.378, + "pct_cuda_time": 0.33714897189548876, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 465.953, + "cuda_time_us": 85.215, + "pct_cuda_time": 0.062405565948142776, + "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": 85.215, + "pct_cuda_time": 0.062405565948142776, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 694.701, + "cuda_time_us": 127.48599999999999, + "pct_cuda_time": 0.0933619196205472, + "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": 34.592, + "pct_cuda_time": 0.025332785745210994, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 91.359, + "pct_cuda_time": 0.06690500615450773, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.535, + "pct_cuda_time": 0.0011241277208284828, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 194.183, + "cuda_time_us": 291.90000000000003, + "pct_cuda_time": 0.21376734964810043, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.728, + "pct_cuda_time": 0.0012654675580401422, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 290.172, + "pct_cuda_time": 0.2125018820900603, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.985, + "cuda_time_us": 65.567, + "pct_cuda_time": 0.04801673112153819, + "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": 65.567, + "pct_cuda_time": 0.04801673112153819, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 461.298, + "cuda_time_us": 3268.693, + "pct_cuda_time": 2.3937644379009875, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.556, + "cuda_time_us": 2074.469, + "pct_cuda_time": 1.5191974650810045, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.0009139487919178805, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2073.221, + "pct_cuda_time": 1.5182835162890866, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.223, + "cuda_time_us": 268.06, + "pct_cuda_time": 0.19630858426402809, + "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": 268.06, + "pct_cuda_time": 0.19630858426402809, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 150.587, + "cuda_time_us": 926.164, + "pct_cuda_time": 0.678258388555955, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 925.428, + "pct_cuda_time": 0.6777193931145675, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2456.897, + "cuda_time_us": 4362.4039999999995, + "pct_cuda_time": 3.194722648764206, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.227, + "cuda_time_us": 66.847, + "pct_cuda_time": 0.04895411449786423, + "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": 66.847, + "pct_cuda_time": 0.04895411449786423, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1738.06, + "cuda_time_us": 965.4269999999999, + "pct_cuda_time": 0.7070118912939932, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.968, + "cuda_time_us": 461.81800000000004, + "pct_cuda_time": 0.3382035281938556, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.184, + "pct_cuda_time": 0.0008670796231015789, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 460.634, + "pct_cuda_time": 0.337336448570754, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 506.818, + "cuda_time_us": 84.831, + "pct_cuda_time": 0.062124350935244974, + "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": 84.831, + "pct_cuda_time": 0.062124350935244974, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 722.204, + "cuda_time_us": 127.83800000000001, + "pct_cuda_time": 0.09361970004903686, + "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": 34.495, + "pct_cuda_time": 0.025261749661223784, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 91.775, + "pct_cuda_time": 0.06720965575181369, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 0.0011482946359993884, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 216.628, + "cuda_time_us": 290.94, + "pct_cuda_time": 0.21306431211585586, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 290.204, + "pct_cuda_time": 0.21252531667446842, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.409, + "cuda_time_us": 64.639, + "pct_cuda_time": 0.04733712817370182, + "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": 64.639, + "pct_cuda_time": 0.04733712817370182, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 487.295, + "cuda_time_us": 3265.491, + "pct_cuda_time": 2.391419514798647, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 175.209, + "cuda_time_us": 2071.268, + "pct_cuda_time": 1.5168532743094267, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2070.532, + "pct_cuda_time": 1.5163142788680393, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.126, + "cuda_time_us": 268.54, + "pct_cuda_time": 0.19666010303015036, + "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": 268.54, + "pct_cuda_time": 0.19666010303015036, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.614, + "cuda_time_us": 925.683, + "pct_cuda_time": 0.67790613745907, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.000960817960734182, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 924.371, + "pct_cuda_time": 0.6769453194983358, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2475.675, + "cuda_time_us": 4366.9490000000005, + "pct_cuda_time": 3.1980510920809264, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.189, + "cuda_time_us": 67.519, + "pct_cuda_time": 0.0494462407704354, + "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": 67.519, + "pct_cuda_time": 0.0494462407704354, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1750.367, + "cuda_time_us": 966.321, + "pct_cuda_time": 0.707666594995896, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 158.389, + "cuda_time_us": 461.209, + "pct_cuda_time": 0.33775753875933795, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.216, + "pct_cuda_time": 0.0008905142075097296, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 459.993, + "pct_cuda_time": 0.3368670245518282, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 491.65, + "cuda_time_us": 84.575, + "pct_cuda_time": 0.06193687425997976, + "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": 84.575, + "pct_cuda_time": 0.06193687425997976, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 744.163, + "cuda_time_us": 128.92600000000002, + "pct_cuda_time": 0.094416475918914, + "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": 35.072, + "pct_cuda_time": 0.02568430451133326, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 92.126, + "pct_cuda_time": 0.0674667038495406, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.728, + "pct_cuda_time": 0.0012654675580401422, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 202.958, + "cuda_time_us": 291.611, + "pct_cuda_time": 0.21355570605766427, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.311, + "pct_cuda_time": 0.0009600856299714273, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 290.3, + "pct_cuda_time": 0.21259562042769287, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.198, + "cuda_time_us": 64.576, + "pct_cuda_time": 0.04729099133564827, + "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": 64.576, + "pct_cuda_time": 0.04729099133564827, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 497.614, + "cuda_time_us": 3268.533, + "pct_cuda_time": 2.393647264978947, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 187.958, + "cuda_time_us": 2073.221, + "pct_cuda_time": 1.5182835162890866, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.728, + "pct_cuda_time": 0.0012654675580401422, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2071.493, + "pct_cuda_time": 1.5170180487310465, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.969, + "cuda_time_us": 269.212, + "pct_cuda_time": 0.19715222930272153, + "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": 269.212, + "pct_cuda_time": 0.19715222930272153, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.028, + "cuda_time_us": 926.1, + "pct_cuda_time": 0.6782115193871388, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 925.364, + "pct_cuda_time": 0.6776725239457513, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2376.798, + "cuda_time_us": 4368.4220000000005, + "pct_cuda_time": 3.1991298152944645, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.511, + "cuda_time_us": 66.591, + "pct_cuda_time": 0.04876663782259902, + "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": 66.591, + "pct_cuda_time": 0.04876663782259902, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1677.182, + "cuda_time_us": 967.219, + "pct_cuda_time": 0.7083242280208498, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.024, + "cuda_time_us": 462.16999999999996, + "pct_cuda_time": 0.3384613086223452, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.664, + "pct_cuda_time": 0.0012185983892238406, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 460.506, + "pct_cuda_time": 0.33724271023312136, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 469.919, + "cuda_time_us": 84.991, + "pct_cuda_time": 0.06224152385728572, + "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": 84.991, + "pct_cuda_time": 0.06224152385728572, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 705.66, + "cuda_time_us": 128.734, + "pct_cuda_time": 0.09427586841246509, + "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": 35.36, + "pct_cuda_time": 0.025895215771006615, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 91.678, + "pct_cuda_time": 0.06713861966782647, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.696, + "pct_cuda_time": 0.0012420329736319913, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 207.106, + "cuda_time_us": 291.324, + "pct_cuda_time": 0.2133455271287537, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 290.588, + "pct_cuda_time": 0.21280653168736624, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.011, + "cuda_time_us": 64.255, + "pct_cuda_time": 0.047055913160804014, + "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": 64.255, + "pct_cuda_time": 0.047055913160804014, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 473.193, + "cuda_time_us": 3270.357, + "pct_cuda_time": 2.394983036290211, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 170.299, + "cuda_time_us": 2076.165, + "pct_cuda_time": 1.5204394980546365, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0005624300257956187, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2075.397, + "pct_cuda_time": 1.5198770680288407, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.105, + "cuda_time_us": 268.86, + "pct_cuda_time": 0.19689444887423185, + "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": 268.86, + "pct_cuda_time": 0.19689444887423185, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 153.66, + "cuda_time_us": 925.332, + "pct_cuda_time": 0.6776490893613432, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.0009139487919178805, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 924.084, + "pct_cuda_time": 0.6767351405694252, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2419.996, + "cuda_time_us": 4369.157999999999, + "pct_cuda_time": 3.1996688107358513, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.886, + "cuda_time_us": 67.583, + "pct_cuda_time": 0.0494931099392517, + "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": 67.583, + "pct_cuda_time": 0.0494931099392517, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1737.95, + "cuda_time_us": 965.9069999999999, + "pct_cuda_time": 0.7073634100601155, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.314, + "cuda_time_us": 461.46599999999995, + "pct_cuda_time": 0.33794574776536584, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0009373833763260313, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 460.186, + "pct_cuda_time": 0.3370083643890398, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 510.677, + "cuda_time_us": 84.798, + "pct_cuda_time": 0.06210018402007406, + "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": 84.798, + "pct_cuda_time": 0.06210018402007406, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 727.78, + "cuda_time_us": 127.77499999999999, + "pct_cuda_time": 0.0935735632109833, + "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": 34.496, + "pct_cuda_time": 0.02526248199198655, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 91.615, + "pct_cuda_time": 0.06709248282977293, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 0.0012185983892238406, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 203.453, + "cuda_time_us": 291.868, + "pct_cuda_time": 0.21374391506369228, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.0009139487919178805, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 290.62, + "pct_cuda_time": 0.21282996627177436, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.551, + "cuda_time_us": 64.639, + "pct_cuda_time": 0.04733712817370182, + "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": 64.639, + "pct_cuda_time": 0.04733712817370182, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 463.219, + "cuda_time_us": 3271.029, + "pct_cuda_time": 2.3954751625627826, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.703, + "cuda_time_us": 2074.98, + "pct_cuda_time": 1.5195716861007722, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2074.244, + "pct_cuda_time": 1.5190326906593847, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.35, + "cuda_time_us": 268.381, + "pct_cuda_time": 0.1965436624388723, + "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": 268.381, + "pct_cuda_time": 0.1965436624388723, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.907, + "cuda_time_us": 927.668, + "pct_cuda_time": 0.679359814023138, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.0011014254671830868, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 926.164, + "pct_cuda_time": 0.678258388555955, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2510.782, + "cuda_time_us": 4364.1, + "pct_cuda_time": 3.1959646817378387, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.831, + "cuda_time_us": 66.495, + "pct_cuda_time": 0.04869633406937457, + "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": 66.495, + "pct_cuda_time": 0.04869633406937457, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1833.893, + "cuda_time_us": 966.865, + "pct_cuda_time": 0.7080649829308345, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.845, + "cuda_time_us": 461.594, + "pct_cuda_time": 0.3380394861029985, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.152, + "pct_cuda_time": 0.0008436450386934281, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 460.442, + "pct_cuda_time": 0.33719584106430506, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 480.971, + "cuda_time_us": 85.151, + "pct_cuda_time": 0.06235869677932648, + "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": 85.151, + "pct_cuda_time": 0.06235869677932648, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 854.57, + "cuda_time_us": 128.444, + "pct_cuda_time": 0.0940634924912662, + "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": 34.719, + "pct_cuda_time": 0.025425791752080845, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 92.19, + "pct_cuda_time": 0.06751357301835689, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.535, + "pct_cuda_time": 0.0011241277208284828, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 202.771, + "cuda_time_us": 291.676, + "pct_cuda_time": 0.21360330755724335, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.704, + "pct_cuda_time": 0.0005155608569793171, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 290.972, + "pct_cuda_time": 0.21308774670026404, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.046, + "cuda_time_us": 64.927, + "pct_cuda_time": 0.04754803943337519, + "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": 64.927, + "pct_cuda_time": 0.04754803943337519, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 460.444, + "cuda_time_us": 3265.813, + "pct_cuda_time": 2.3916553253042543, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 166.832, + "cuda_time_us": 2073.925, + "pct_cuda_time": 1.5187990771460662, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0009373833763260313, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2072.645, + "pct_cuda_time": 1.51786169376974, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.912, + "cuda_time_us": 268.476, + "pct_cuda_time": 0.19661323386133403, + "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": 268.476, + "pct_cuda_time": 0.19661323386133403, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 149.498, + "cuda_time_us": 923.412, + "pct_cuda_time": 0.6762430142968541, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.000960817960734182, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 922.1, + "pct_cuda_time": 0.6752821963361199, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2592.576, + "cuda_time_us": 4340.422, + "pct_cuda_time": 3.178624553937332, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.97, + "cuda_time_us": 66.591, + "pct_cuda_time": 0.04876663782259902, + "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": 66.591, + "pct_cuda_time": 0.04876663782259902, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1875.953, + "cuda_time_us": 961.939, + "pct_cuda_time": 0.7044575215935048, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 186.708, + "cuda_time_us": 459.066, + "pct_cuda_time": 0.33618815393475454, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.0009139487919178805, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 457.818, + "pct_cuda_time": 0.3352742051428367, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 553.216, + "cuda_time_us": 85.567, + "pct_cuda_time": 0.06266334637663243, + "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": 85.567, + "pct_cuda_time": 0.06266334637663243, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 762.978, + "cuda_time_us": 127.998, + "pct_cuda_time": 0.09373687297107762, + "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": 34.495, + "pct_cuda_time": 0.025261749661223784, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 91.743, + "pct_cuda_time": 0.06718622116740552, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.76, + "pct_cuda_time": 0.001288902142448293, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 226.62, + "cuda_time_us": 289.308, + "pct_cuda_time": 0.21186914831104017, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.000960817960734182, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 287.996, + "pct_cuda_time": 0.21090833035030598, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 90.206, + "cuda_time_us": 65.375, + "pct_cuda_time": 0.04787612361508929, + "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": 65.375, + "pct_cuda_time": 0.04787612361508929, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 485.372, + "cuda_time_us": 3246.517, + "pct_cuda_time": 2.3775242709061386, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 173.305, + "cuda_time_us": 2058.3089999999997, + "pct_cuda_time": 1.507362999954888, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2057.573, + "pct_cuda_time": 1.5068240045135006, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.953, + "cuda_time_us": 267.932, + "pct_cuda_time": 0.1962148459263955, + "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": 267.932, + "pct_cuda_time": 0.1962148459263955, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.377, + "cuda_time_us": 920.276, + "pct_cuda_time": 0.6739464250248552, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 919.54, + "pct_cuda_time": 0.6734074295834678, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2446.265, + "cuda_time_us": 4361.573, + "pct_cuda_time": 3.1941140819003575, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.916, + "cuda_time_us": 67.071, + "pct_cuda_time": 0.049118156588721276, + "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": 67.071, + "pct_cuda_time": 0.049118156588721276, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1753.735, + "cuda_time_us": 963.7950000000001, + "pct_cuda_time": 0.7058167274891777, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.528, + "cuda_time_us": 459.738, + "pct_cuda_time": 0.33668028020732577, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.76, + "pct_cuda_time": 0.001288902142448293, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 457.978, + "pct_cuda_time": 0.3353913780648775, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 521.278, + "cuda_time_us": 84.639, + "pct_cuda_time": 0.061983743428796055, + "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": 84.639, + "pct_cuda_time": 0.061983743428796055, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 722.606, + "cuda_time_us": 128.478, + "pct_cuda_time": 0.09408839173719989, + "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": 35.039, + "pct_cuda_time": 0.02566013759616235, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 91.679, + "pct_cuda_time": 0.06713935199858924, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.76, + "pct_cuda_time": 0.001288902142448293, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 203.79, + "cuda_time_us": 290.94, + "pct_cuda_time": 0.21306431211585586, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.000538995441387468, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 290.204, + "pct_cuda_time": 0.21252531667446842, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.097, + "cuda_time_us": 64.223, + "pct_cuda_time": 0.047032478576395864, + "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": 64.223, + "pct_cuda_time": 0.047032478576395864, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 467.398, + "cuda_time_us": 3266.484, + "pct_cuda_time": 2.392146719246062, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 166.0, + "cuda_time_us": 2071.045, + "pct_cuda_time": 1.5166899645493326, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0009373833763260313, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2069.765, + "pct_cuda_time": 1.5157525811730064, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.361, + "cuda_time_us": 268.7, + "pct_cuda_time": 0.19677727595219108, + "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": 268.7, + "pct_cuda_time": 0.19677727595219108, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 153.823, + "cuda_time_us": 926.7389999999999, + "pct_cuda_time": 0.6786794787445389, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0009373833763260313, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 925.459, + "pct_cuda_time": 0.677742095368213, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2461.257, + "cuda_time_us": 4361.32, + "pct_cuda_time": 3.19392880221738, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.249, + "cuda_time_us": 67.328, + "pct_cuda_time": 0.04930636559474925, + "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": 67.328, + "pct_cuda_time": 0.04930636559474925, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1778.322, + "cuda_time_us": 962.6770000000001, + "pct_cuda_time": 0.7049979816964179, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.074, + "cuda_time_us": 459.09700000000004, + "pct_cuda_time": 0.3362108561884, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.247, + "pct_cuda_time": 0.0009132164611551259, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 457.85, + "pct_cuda_time": 0.33529763972724486, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 511.938, + "cuda_time_us": 84.736, + "pct_cuda_time": 0.06205477951278328, + "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": 84.736, + "pct_cuda_time": 0.06205477951278328, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 776.273, + "cuda_time_us": 127.32700000000001, + "pct_cuda_time": 0.09324547902926922, + "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": 35.008, + "pct_cuda_time": 0.02563743534251696, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 90.815, + "pct_cuda_time": 0.06650661821956917, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 0.0011014254671830868, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 201.85, + "cuda_time_us": 291.517, + "pct_cuda_time": 0.21348686696596536, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.793, + "pct_cuda_time": 0.0013130690576191985, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 289.724, + "pct_cuda_time": 0.21217379790834617, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.506, + "cuda_time_us": 64.671, + "pct_cuda_time": 0.04736056275810998, + "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": 64.671, + "pct_cuda_time": 0.04736056275810998, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 465.827, + "cuda_time_us": 3266.644, + "pct_cuda_time": 2.3922638921681028, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 166.815, + "cuda_time_us": 2071.236, + "pct_cuda_time": 1.5168298397250184, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0005624300257956187, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2070.468, + "pct_cuda_time": 1.5162674096992228, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.176, + "cuda_time_us": 268.285, + "pct_cuda_time": 0.1964733586856479, + "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": 268.285, + "pct_cuda_time": 0.1964733586856479, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.225, + "cuda_time_us": 927.123, + "pct_cuda_time": 0.6789606937574368, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0005624300257956187, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 926.355, + "pct_cuda_time": 0.6783982637316411, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2437.97, + "cuda_time_us": 4345.607, + "pct_cuda_time": 3.1824216889422154, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.957, + "cuda_time_us": 67.232, + "pct_cuda_time": 0.04923606184152479, + "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": 67.232, + "pct_cuda_time": 0.04923606184152479, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1757.832, + "cuda_time_us": 961.364, + "pct_cuda_time": 0.7040364314049209, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.099, + "cuda_time_us": 458.362, + "pct_cuda_time": 0.33567259307777525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.12, + "pct_cuda_time": 0.0008202104542852774, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 457.242, + "pct_cuda_time": 0.33485238262349, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 526.505, + "cuda_time_us": 86.079, + "pct_cuda_time": 0.06303829972716284, + "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": 86.079, + "pct_cuda_time": 0.06303829972716284, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 729.294, + "cuda_time_us": 127.007, + "pct_cuda_time": 0.0930111331851877, + "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": 34.208, + "pct_cuda_time": 0.025051570732313184, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 91.167, + "pct_cuda_time": 0.06676439864805882, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.632, + "pct_cuda_time": 0.0011951638048156897, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 200.818, + "cuda_time_us": 289.916, + "pct_cuda_time": 0.21231440541479504, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0005624300257956187, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 289.148, + "pct_cuda_time": 0.21175197538899948, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.903, + "cuda_time_us": 65.503, + "pct_cuda_time": 0.0479698619527219, + "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": 65.503, + "pct_cuda_time": 0.0479698619527219, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 457.816, + "cuda_time_us": 3251.508, + "pct_cuda_time": 2.381179333743048, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.009, + "cuda_time_us": 2062.789, + "pct_cuda_time": 1.5106438417720296, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.536, + "pct_cuda_time": 0.0011248600515912375, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2061.253, + "pct_cuda_time": 1.5095189817204384, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.318, + "cuda_time_us": 267.836, + "pct_cuda_time": 0.19614454217317104, + "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": 267.836, + "pct_cuda_time": 0.19614454217317104, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 149.829, + "cuda_time_us": 920.883, + "pct_cuda_time": 0.6743909497978474, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.0009139487919178805, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 919.635, + "pct_cuda_time": 0.6734770010059294, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2451.419, + "cuda_time_us": 4344.7119999999995, + "pct_cuda_time": 3.1817662529095494, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.818, + "cuda_time_us": 66.336, + "pct_cuda_time": 0.04857989347809657, + "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": 66.336, + "pct_cuda_time": 0.04857989347809657, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1700.701, + "cuda_time_us": 962.3879999999999, + "pct_cuda_time": 0.7047863381059816, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.258, + "cuda_time_us": 459.83299999999997, + "pct_cuda_time": 0.33674985162978743, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.311, + "pct_cuda_time": 0.0009600856299714273, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 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": 458.522, + "pct_cuda_time": 0.335789765999816, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6144, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 511.101, + "cuda_time_us": 85.311, + "pct_cuda_time": 0.06247586970136724, + "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": 85.311, + "pct_cuda_time": 0.06247586970136724, + "trace": "_C::rotary_embedding(int64[6144], bfloat16[6144, 4096], bfloat16[6144, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 704.428, + "cuda_time_us": 127.487, + "pct_cuda_time": 0.09336265195130995, + "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": 34.464, + "pct_cuda_time": 0.025239047407578392, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6144], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 91.295, + "pct_cuda_time": 0.06685813698569143, + "trace": "_vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 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": 1.728, + "pct_cuda_time": 0.0012654675580401422, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], None, None, bfloat16[6144, 32, 128], int32[25], int32[25], None, None, None, 256, 256, 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[6144, 32, 128], bfloat16[6144, 8, 128], bfloat16[6144, 8, 128], bfloat16[6144, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 199.078, + "cuda_time_us": 289.757, + "pct_cuda_time": 0.21219796482351708, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.216, + "pct_cuda_time": 0.0008905142075097296, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 288.541, + "pct_cuda_time": 0.21130745061600734, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6144, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 96.32, + "cuda_time_us": 64.831, + "pct_cuda_time": 0.04747773568015073, + "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": 64.831, + "pct_cuda_time": 0.04747773568015073, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 516.602, + "cuda_time_us": 3251.157, + "pct_cuda_time": 2.380922285645321, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 193.887, + "cuda_time_us": 2061.9880000000003, + "pct_cuda_time": 1.5100572448310632, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.568, + "pct_cuda_time": 0.0011482946359993884, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 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": 2060.42, + "pct_cuda_time": 1.5089089501950637, + "trace": "mm(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6144, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6144, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.745, + "cuda_time_us": 268.381, + "pct_cuda_time": 0.1965436624388723, + "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": 268.381, + "pct_cuda_time": 0.1965436624388723, + "trace": "_C::silu_and_mul(bfloat16[6144, 14336], bfloat16[6144, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 158.165, + "cuda_time_us": 920.788, + "pct_cuda_time": 0.6743213783753856, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0005624300257956187, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 920.02, + "pct_cuda_time": 0.67375894834959, + "trace": "mm(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6144, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6144, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.437, + "cuda_time_us": 67.071, + "pct_cuda_time": 0.049118156588721276, + "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": 67.071, + "pct_cuda_time": 0.049118156588721276, + "trace": "_C::fused_add_rms_norm(bfloat16[6144, 4096], bfloat16[6144, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 478.946, + "cuda_time_us": 406.298, + "pct_cuda_time": 0.297544524245714, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 4.448, + "pct_cuda_time": 0.0032574072327329587, + "trace": "index_select(bfloat16[6144, 4096], 0, int64[24])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.0009373833763260313, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[24, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 400.57, + "pct_cuda_time": 0.29334973363665495, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[24, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 58988.493, + "cuda_time_us": 172.988, + "pct_cuda_time": 0.1266844339874121, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 3.392, + "pct_cuda_time": 0.0024840659472639827, + "trace": "copy_(bfloat16[24], bfloat16[24], True) <- _to_copy(bfloat16[24], 15, 0, None, None, True, None) <- to(bfloat16[24], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.0017810284150194592, + "trace": "copy_(bfloat16[24], bfloat16[24], True) <- _to_copy(bfloat16[24], 15, 0, None, None, True, None) <- to(bfloat16[24], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.592, + "pct_cuda_time": 0.0018982013370602133, + "trace": "copy_(int32[24], int32[24], True) <- _to_copy(int32[24], 3, 0, None, None, True, None) <- to(int32[24], 3, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.0018044629994276103, + "trace": "copy_(bfloat16[24], bfloat16[24], True) <- _to_copy(bfloat16[24], 15, 0, None, None, True, None) <- to(bfloat16[24], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.0018044629994276103, + "trace": "copy_(bfloat16[24], bfloat16[24], True) <- _to_copy(bfloat16[24], 15, 0, None, None, True, None) <- to(bfloat16[24], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.001827897583835761, + "trace": "copy_(bfloat16[24], bfloat16[24], True) <- _to_copy(bfloat16[24], 15, 0, None, None, True, None) <- to(bfloat16[24], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.0018513321682439117, + "trace": "copy_(bfloat16[24], bfloat16[24], True) <- _to_copy(bfloat16[24], 15, 0, None, None, True, None) <- to(bfloat16[24], 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": 10.879, + "pct_cuda_time": 0.007967026368008511, + "trace": "copy_(float32[24, 128256], bfloat16[24, 128256], False) <- _to_copy(bfloat16[24, 128256], 6, None, None, None, False, None) <- to(bfloat16[24, 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": 17.759, + "pct_cuda_time": 0.01300546201576093, + "trace": "div_(float32[24, 128256], bfloat16[24, 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": 39.616, + "pct_cuda_time": 0.02901201549729067, + "trace": "_softmax(float32[24, 128256], -1, False) <- softmax(float32[24, 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": 32.127, + "pct_cuda_time": 0.023527590415020633, + "trace": "_log_softmax(float32[24, 128256], -1, False) <- log_softmax(float32[24, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.0015466825709379516, + "trace": "copy_(int64[24], int32[24], False) <- _to_copy(int32[24], 4, None, None, None, False, None) <- to(int32[24], 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": 17.056, + "pct_cuda_time": 0.012490633489544366, + "trace": "index(float32[24, 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": 31.903, + "pct_cuda_time": 0.023363548324163576, + "trace": "argmax(float32[24, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0023200238564069276, + "trace": "copy_(int64[24], int64[24], False) <- _to_copy(int64[24], 4, 0, None, None, False, None) <- to(int64[24], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + }, + "decode_1": { + "metadata": { + "num_running_seqs": 24 + }, + "summary_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cuda_time_us": 6848.458000000001, + "pct_cuda_time": 92.7556555766421, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cuda_time_us": 3.584, + "pct_cuda_time": 0.04854176948835565, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", + "cuda_time_us": 3.584, + "pct_cuda_time": 0.04854176948835565, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cuda_time_us": 6841.546000000001, + "pct_cuda_time": 92.66203930691455, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cuda_time_us": 198.23900000000006, + "pct_cuda_time": 2.6849530808041684, + "invocations": 64 + }, + "children": [ + { + "entry": { + "name": "void vllm::rms_norm_kernel(c10::BFloat16*, c10::BFloat16 const*, c10::BFloat16 const*, float, int, int)", + "cuda_time_us": 4.352, + "pct_cuda_time": 0.05894357723586044, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cuda_time_us": 193.88700000000006, + "pct_cuda_time": 2.6260095035683078, + "invocations": 63 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cuda_time_us": 2218.5319999999997, + "pct_cuda_time": 30.047842898030314, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cuda_time_us": 711.22, + "pct_cuda_time": 9.632778263255668, + "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": 711.22, + "pct_cuda_time": 9.632778263255668, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cuda_time_us": 119.55000000000004, + "pct_cuda_time": 1.6191876513205694, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cuda_time_us": 119.55000000000004, + "pct_cuda_time": 1.6191876513205694, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cuda_time_us": 854.744, + "pct_cuda_time": 11.576670262152641, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cuda_time_us": 78.65700000000002, + "pct_cuda_time": 1.0653320208274533, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cuda_time_us": 728.1519999999999, + "pct_cuda_time": 9.862105618438935, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": 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vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cuda_time_us": 3.328, + "pct_cuda_time": 0.045074500239187396, + "invocations": 1 + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cuda_time_us": 390.908, + "pct_cuda_time": 5.294465967397917, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", + "cuda_time_us": 3.552, + "pct_cuda_time": 0.04810836083220962, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cuda_time_us": 0.769, + "pct_cuda_time": 0.010415351768009347, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cuda_time_us": 386.587, + "pct_cuda_time": 5.235942254797697, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cuda_time_us": 143.966, + "pct_cuda_time": 1.9498784559599918, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cuda_time_us": 5.183999999999999, + "pct_cuda_time": 0.07021220229565728, + "invocations": 7 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cuda_time_us": 9.888, + "pct_cuda_time": 0.13392327474912408, + "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": 15.36, + "pct_cuda_time": 0.20803615495009567, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::SoftMaxForwardEpilogue>(float*, float const*, int)", + "cuda_time_us": 35.455, + "pct_cuda_time": 0.48020324698929956, + "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": 30.591, + "pct_cuda_time": 0.4143251312551026, + "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": 1.888, + "pct_cuda_time": 0.025571110712615922, + "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": 15.52, + "pct_cuda_time": 0.2102031982308258, + "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": 27.584, + "pct_cuda_time": 0.3735982615978801, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cuda_time_us": 2.496, + "pct_cuda_time": 0.03380587517939055, + "invocations": 1 + }, + "children": [] + } + ] + } + ], + "model_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cpu_time_us": 81695.728, + "cuda_time_us": 6848.458000000001, + "pct_cuda_time": 92.7556555766421, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cpu_time_us": 389.493, + "cuda_time_us": 3.584, + "pct_cuda_time": 0.04854176948835565, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 3.584, + "pct_cuda_time": 0.04854176948835565, + "trace": "index_select(bfloat16[128256, 4096], 0, int64[24]) <- embedding(bfloat16[128256, 4096], int64[24], -1, False, False)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 5418.206, + "cuda_time_us": 219.836, + "pct_cuda_time": 2.977463291641226, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 406.562, + "cuda_time_us": 4.352, + "pct_cuda_time": 0.05894357723586044, + "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": 4.352, + "pct_cuda_time": 0.05894357723586044, + "trace": "_C::rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 3876.236, + "cuda_time_us": 74.814, + "pct_cuda_time": 1.013282350028415, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 833.321, + "cuda_time_us": 26.495, + "pct_cuda_time": 0.35884882326841044, + "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": 26.495, + "pct_cuda_time": 0.35884882326841044, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 1101.535, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.049408586800647726, + "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": 0.049408586800647726, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1265.617, + "cuda_time_us": 28.064, + "pct_cuda_time": 0.3800993914400706, + "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": 2.304, + "pct_cuda_time": 0.031205423242514345, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 24.096, + "pct_cuda_time": 0.32635671807796257, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.022537250119593698, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 302.813, + "cuda_time_us": 16.607, + "pct_cuda_time": 0.22492554851928637, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.607, + "pct_cuda_time": 0.22492554851928637, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 155.408, + "cuda_time_us": 2.912, + "pct_cuda_time": 0.03944018770928897, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.912, + "pct_cuda_time": 0.03944018770928897, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 833.228, + "cuda_time_us": 137.758, + "pct_cuda_time": 1.8657971766676618, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 283.548, + "cuda_time_us": 82.111, + "pct_cuda_time": 1.1121130676502153, + "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": 82.111, + "pct_cuda_time": 1.1121130676502153, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 191.669, + "cuda_time_us": 9.408, + "pct_cuda_time": 0.12742214490693357, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.408, + "pct_cuda_time": 0.12742214490693357, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 264.213, + "cuda_time_us": 46.239000000000004, + "pct_cuda_time": 0.6262619641105127, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.904, + "pct_cuda_time": 0.5946366762323568, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.335, + "pct_cuda_time": 0.03162528787815582, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2636.687, + "cuda_time_us": 214.237, + "pct_cuda_time": 2.901630320836175, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.066, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04334086561460327, + "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": 0.04334086561460327, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1919.883, + "cuda_time_us": 68.799, + "pct_cuda_time": 0.9318150666934657, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 173.191, + "cuda_time_us": 22.24, + "pct_cuda_time": 0.30121901602149265, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.24, + "pct_cuda_time": 0.30121901602149265, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 557.808, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.049841995456793756, + "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": 0.049841995456793756, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 811.649, + "cuda_time_us": 26.303, + "pct_cuda_time": 0.3562483713315343, + "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": 2.4, + "pct_cuda_time": 0.03250564921095245, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.431, + "pct_cuda_time": 0.3038059239378643, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.0199367981827175, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 214.389, + "cuda_time_us": 16.576, + "pct_cuda_time": 0.22450568388364492, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.576, + "pct_cuda_time": 0.22450568388364492, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.534, + "cuda_time_us": 2.848, + "pct_cuda_time": 0.0385733703969969, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.848, + "pct_cuda_time": 0.0385733703969969, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 466.543, + "cuda_time_us": 139.39, + "pct_cuda_time": 1.8879010181311089, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 151.144, + "cuda_time_us": 83.806, + "pct_cuda_time": 1.1350701824054503, + "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": 83.806, + "pct_cuda_time": 1.1350701824054503, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.477, + "cuda_time_us": 9.152, + "pct_cuda_time": 0.12395487565776532, + "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": 9.152, + "pct_cuda_time": 0.12395487565776532, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 145.696, + "cuda_time_us": 46.432, + "pct_cuda_time": 0.6288759600678934, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.776, + "pct_cuda_time": 0.5929030416077727, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.656, + "pct_cuda_time": 0.03597291846012071, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2501.7, + "cuda_time_us": 213.30700000000002, + "pct_cuda_time": 2.889034381766931, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.398, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04204063964616517, + "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": 0.04204063964616517, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1794.426, + "cuda_time_us": 69.405, + "pct_cuda_time": 0.9400227431192312, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 143.596, + "cuda_time_us": 22.463, + "pct_cuda_time": 0.30423933259401037, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.463, + "pct_cuda_time": 0.30423933259401037, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 514.365, + "cuda_time_us": 3.679, + "pct_cuda_time": 0.04982845143628919, + "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": 0.04982845143628919, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 768.55, + "cuda_time_us": 26.462999999999997, + "pct_cuda_time": 0.3584154146122644, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.56, + "pct_cuda_time": 0.03467269249168261, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.4, + "pct_cuda_time": 0.30338605930222284, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.02035666281835897, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 204.9, + "cuda_time_us": 16.8, + "pct_cuda_time": 0.22753954447666713, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.8, + "pct_cuda_time": 0.22753954447666713, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.834, + "cuda_time_us": 3.039, + "pct_cuda_time": 0.04116027831336854, + "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": 3.039, + "pct_cuda_time": 0.04116027831336854, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 468.313, + "cuda_time_us": 137.75900000000001, + "pct_cuda_time": 1.8658107206881662, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 148.694, + "cuda_time_us": 81.599, + "pct_cuda_time": 1.1051785291518788, + "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": 81.599, + "pct_cuda_time": 1.1051785291518788, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.093, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.12438828431391136, + "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": 0.12438828431391136, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 161.328, + "cuda_time_us": 46.976, + "pct_cuda_time": 0.6362439072223759, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.48, + "pct_cuda_time": 0.6024380320429853, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.03380587517939055, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2421.484, + "cuda_time_us": 214.81199999999998, + "pct_cuda_time": 2.9094181326262984, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.012, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.042474048302311204, + "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": 0.042474048302311204, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1752.173, + "cuda_time_us": 69.695, + "pct_cuda_time": 0.9439505090655544, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 152.548, + "cuda_time_us": 21.727, + "pct_cuda_time": 0.2942709335026516, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.727, + "pct_cuda_time": 0.2942709335026516, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 539.418, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05200903873752392, + "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": 0.05200903873752392, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 724.453, + "cuda_time_us": 26.848, + "pct_cuda_time": 0.36362986250652135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.624, + "pct_cuda_time": 0.035539509803974675, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.784, + "pct_cuda_time": 0.3085869631759752, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.019503389526571466, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 181.355, + "cuda_time_us": 17.28, + "pct_cuda_time": 0.23404067431885764, + "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": 17.28, + "pct_cuda_time": 0.23404067431885764, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.396, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.040307005021581035, + "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": 0.040307005021581035, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 444.347, + "cuda_time_us": 139.005, + "pct_cuda_time": 1.8826865702368518, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 144.269, + "cuda_time_us": 82.303, + "pct_cuda_time": 1.1147135195870914, + "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": 82.303, + "pct_cuda_time": 1.1147135195870914, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.397, + "cuda_time_us": 9.535, + "pct_cuda_time": 0.12914223551101317, + "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": 9.535, + "pct_cuda_time": 0.12914223551101317, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 139.806, + "cuda_time_us": 47.167, + "pct_cuda_time": 0.6388308151387476, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.703, + "pct_cuda_time": 0.6054583486155031, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033372466523244514, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2351.894, + "cuda_time_us": 214.23699999999997, + "pct_cuda_time": 2.9016303208361744, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.518, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.042907456958457234, + "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": 0.042907456958457234, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1654.387, + "cuda_time_us": 69.50399999999999, + "pct_cuda_time": 0.9413636011491828, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 147.496, + "cuda_time_us": 22.784, + "pct_cuda_time": 0.3085869631759752, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.784, + "pct_cuda_time": 0.3085869631759752, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 478.676, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.05070881276908583, + "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": 0.05070881276908583, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 689.773, + "cuda_time_us": 26.592, + "pct_cuda_time": 0.3601625932573531, + "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": 2.433, + "pct_cuda_time": 0.032952601887603045, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.688, + "pct_cuda_time": 0.30728673720753713, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.471, + "pct_cuda_time": 0.01992325416221294, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 177.574, + "cuda_time_us": 16.384, + "pct_cuda_time": 0.2219052319467687, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.384, + "pct_cuda_time": 0.2219052319467687, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 92.833, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04074041367772707, + "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": 0.04074041367772707, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 460.727, + "cuda_time_us": 138.557, + "pct_cuda_time": 1.8766188490508076, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 144.078, + "cuda_time_us": 83.327, + "pct_cuda_time": 1.1285825965837644, + "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": 1.1285825965837644, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 90.743, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.12438828431391136, + "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": 0.12438828431391136, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 159.797, + "cuda_time_us": 46.046, + "pct_cuda_time": 0.6236479681531318, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.551, + "pct_cuda_time": 0.5898556369942459, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.495, + "pct_cuda_time": 0.03379233115888598, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2516.821, + "cuda_time_us": 214.142, + "pct_cuda_time": 2.9003436388882413, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.62, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.0437742742707493, + "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": 0.0437742742707493, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1830.034, + "cuda_time_us": 69.28, + "pct_cuda_time": 0.9383297405561607, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 143.224, + "cuda_time_us": 21.6, + "pct_cuda_time": 0.292550842898572, + "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": 21.6, + "pct_cuda_time": 0.292550842898572, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 498.616, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.050275404112939785, + "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": 0.050275404112939785, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 833.709, + "cuda_time_us": 27.104, + "pct_cuda_time": 0.36709713175568964, + "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": 2.4, + "pct_cuda_time": 0.03250564921095245, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 23.232, + "pct_cuda_time": 0.31465468436201965, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.0199367981827175, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 201.383, + "cuda_time_us": 16.864, + "pct_cuda_time": 0.2284063617889592, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.864, + "pct_cuda_time": 0.2284063617889592, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.996, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04160723099001914, + "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": 0.04160723099001914, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 436.872, + "cuda_time_us": 138.558, + "pct_cuda_time": 1.8766323930713122, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 143.289, + "cuda_time_us": 81.854, + "pct_cuda_time": 1.1086322543805422, + "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": 81.854, + "pct_cuda_time": 1.1086322543805422, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.431, + "cuda_time_us": 9.408, + "pct_cuda_time": 0.12742214490693357, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.408, + "pct_cuda_time": 0.12742214490693357, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.191, + "cuda_time_us": 47.296, + "pct_cuda_time": 0.6405779937838362, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.8, + "pct_cuda_time": 0.6067721186044457, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.03380587517939055, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2349.153, + "cuda_time_us": 213.01999999999998, + "pct_cuda_time": 2.885147247882121, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.39, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.042907456958457234, + "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": 0.042907456958457234, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1686.932, + "cuda_time_us": 68.575, + "pct_cuda_time": 0.9287812061004433, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.348, + "cuda_time_us": 21.696, + "pct_cuda_time": 0.2938510688670101, + "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": 0.2938510688670101, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 477.291, + "cuda_time_us": 3.775, + "pct_cuda_time": 0.05112867740472729, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.775, + "pct_cuda_time": 0.05112867740472729, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 731.268, + "cuda_time_us": 26.624, + "pct_cuda_time": 0.36059600191349916, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.032939057867098484, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.72, + "pct_cuda_time": 0.3077201458636831, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.0199367981827175, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 178.164, + "cuda_time_us": 16.48, + "pct_cuda_time": 0.22320545791520682, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.48, + "pct_cuda_time": 0.22320545791520682, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.627, + "cuda_time_us": 2.911, + "pct_cuda_time": 0.03942664368878441, + "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": 2.911, + "pct_cuda_time": 0.03942664368878441, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 441.136, + "cuda_time_us": 138.36599999999999, + "pct_cuda_time": 1.874031941134436, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 149.636, + "cuda_time_us": 81.823, + "pct_cuda_time": 1.1082123897449008, + "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": 81.823, + "pct_cuda_time": 1.1082123897449008, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.018, + "cuda_time_us": 9.248, + "pct_cuda_time": 0.12525510162620343, + "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": 0.12525510162620343, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.93, + "cuda_time_us": 47.295, + "pct_cuda_time": 0.6405644497633317, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.799, + "pct_cuda_time": 0.6067585745839411, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.03380587517939055, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2264.097, + "cuda_time_us": 215.07, + "pct_cuda_time": 2.912912489916476, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.926, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.042054183666669735, + "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": 0.042054183666669735, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1597.007, + "cuda_time_us": 69.758, + "pct_cuda_time": 0.9448037823573419, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.1, + "cuda_time_us": 22.72, + "pct_cuda_time": 0.3077201458636831, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.72, + "pct_cuda_time": 0.3077201458636831, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 475.887, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.050275404112939785, + "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": 0.050275404112939785, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 674.505, + "cuda_time_us": 26.719, + "pct_cuda_time": 0.36188268386143274, + "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": 2.4, + "pct_cuda_time": 0.03250564921095245, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.879, + "pct_cuda_time": 0.3098736451239088, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.019503389526571466, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 171.91, + "cuda_time_us": 16.607, + "pct_cuda_time": 0.22492554851928637, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.607, + "pct_cuda_time": 0.22492554851928637, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.213, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0411738223338731, + "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": 0.0411738223338731, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 447.178, + "cuda_time_us": 139.167, + "pct_cuda_time": 1.8848807015585913, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 143.82, + "cuda_time_us": 82.718, + "pct_cuda_time": 1.1203342880964853, + "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": 82.718, + "pct_cuda_time": 1.1203342880964853, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.886, + "cuda_time_us": 9.312, + "pct_cuda_time": 0.12612191893849548, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.312, + "pct_cuda_time": 0.12612191893849548, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 149.072, + "cuda_time_us": 47.137, + "pct_cuda_time": 0.6384244945236106, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.672, + "pct_cuda_time": 0.6050384839798615, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.465, + "pct_cuda_time": 0.033386010543749074, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2412.504, + "cuda_time_us": 213.755, + "pct_cuda_time": 2.895102102952975, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.926, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.042474048302311204, + "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": 0.042474048302311204, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1727.136, + "cuda_time_us": 69.502, + "pct_cuda_time": 0.9413365131081738, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.103, + "cuda_time_us": 22.176, + "pct_cuda_time": 0.3003521987092006, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.176, + "pct_cuda_time": 0.3003521987092006, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 515.257, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.049841995456793756, + "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": 0.049841995456793756, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 730.825, + "cuda_time_us": 27.102, + "pct_cuda_time": 0.3670700437146805, + "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": 2.591, + "pct_cuda_time": 0.035092557127324085, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 23.008, + "pct_cuda_time": 0.31162082376899747, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.02035666281835897, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 202.356, + "cuda_time_us": 16.544, + "pct_cuda_time": 0.2240722752274989, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.544, + "pct_cuda_time": 0.2240722752274989, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 88.382, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04074041367772707, + "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": 0.04074041367772707, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 443.04, + "cuda_time_us": 138.109, + "pct_cuda_time": 1.8705511278647633, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 149.711, + "cuda_time_us": 82.047, + "pct_cuda_time": 1.111246250337923, + "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": 82.047, + "pct_cuda_time": 1.111246250337923, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.249, + "cuda_time_us": 8.959, + "pct_cuda_time": 0.12134087970038457, + "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": 0.12134087970038457, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.194, + "cuda_time_us": 47.103, + "pct_cuda_time": 0.6379639978264555, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.447, + "pct_cuda_time": 0.6019910793663349, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.656, + "pct_cuda_time": 0.03597291846012071, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2340.789, + "cuda_time_us": 212.096, + "pct_cuda_time": 2.8726325729359043, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.747, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.042487592322815765, + "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": 0.042487592322815765, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1658.393, + "cuda_time_us": 68.672, + "pct_cuda_time": 0.930094976089386, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 153.739, + "cuda_time_us": 21.568, + "pct_cuda_time": 0.29211743424242603, + "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": 21.568, + "pct_cuda_time": 0.29211743424242603, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 477.375, + "cuda_time_us": 3.713, + "pct_cuda_time": 0.05028894813344435, + "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": 0.05028894813344435, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 694.24, + "cuda_time_us": 26.720000000000002, + "pct_cuda_time": 0.36189622788193726, + "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": 2.625, + "pct_cuda_time": 0.035553053824479236, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.495, + "pct_cuda_time": 0.3046727412501564, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 1.6, + "pct_cuda_time": 0.021670432807301635, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 176.087, + "cuda_time_us": 16.671, + "pct_cuda_time": 0.22579236583157844, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.671, + "pct_cuda_time": 0.22579236583157844, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.576, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04074041367772707, + "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": 0.04074041367772707, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 457.753, + "cuda_time_us": 137.279, + "pct_cuda_time": 1.8593095908459756, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 144.824, + "cuda_time_us": 81.343, + "pct_cuda_time": 1.1017112599027106, + "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": 81.343, + "pct_cuda_time": 1.1017112599027106, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.342, + "cuda_time_us": 9.152, + "pct_cuda_time": 0.12395487565776532, + "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": 9.152, + "pct_cuda_time": 0.12395487565776532, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 161.901, + "cuda_time_us": 46.784, + "pct_cuda_time": 0.6336434552854997, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.224, + "pct_cuda_time": 0.598970762793817, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.56, + "pct_cuda_time": 0.03467269249168261, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2303.252, + "cuda_time_us": 214.94099999999997, + "pct_cuda_time": 2.911165311271387, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.342, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.042907456958457234, + "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": 0.042907456958457234, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1656.806, + "cuda_time_us": 69.791, + "pct_cuda_time": 0.9452507350339926, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 157.967, + "cuda_time_us": 22.304, + "pct_cuda_time": 0.30208583333378475, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.304, + "pct_cuda_time": 0.30208583333378475, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 483.775, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.050275404112939785, + "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": 0.050275404112939785, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 684.16, + "cuda_time_us": 27.039, + "pct_cuda_time": 0.366216770422893, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.032939057867098484, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.911, + "pct_cuda_time": 0.31030705378005485, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 1.696, + "pct_cuda_time": 0.022970658775739727, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 180.995, + "cuda_time_us": 16.736, + "pct_cuda_time": 0.2266727271643751, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.736, + "pct_cuda_time": 0.2266727271643751, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.951, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04074041367772707, + "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": 0.04074041367772707, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 428.484, + "cuda_time_us": 138.974, + "pct_cuda_time": 1.8822667056012106, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 141.121, + "cuda_time_us": 83.007, + "pct_cuda_time": 1.124248510022304, + "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": 83.007, + "pct_cuda_time": 1.124248510022304, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.726, + "cuda_time_us": 9.568, + "pct_cuda_time": 0.12958918818766374, + "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": 9.568, + "pct_cuda_time": 0.12958918818766374, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.152, + "cuda_time_us": 46.399, + "pct_cuda_time": 0.6284290073912427, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.936, + "pct_cuda_time": 0.5950700848885028, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.463, + "pct_cuda_time": 0.03335892250273995, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2416.844, + "cuda_time_us": 212.76600000000002, + "pct_cuda_time": 2.8817070666739624, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.147, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.042907456958457234, + "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": 0.042907456958457234, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1730.657, + "cuda_time_us": 68.41499999999999, + "pct_cuda_time": 0.9266141628197132, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.449, + "cuda_time_us": 21.631, + "pct_cuda_time": 0.2929707075342135, + "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": 21.631, + "pct_cuda_time": 0.2929707075342135, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 443.44, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.050275404112939785, + "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": 0.050275404112939785, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 747.799, + "cuda_time_us": 26.528, + "pct_cuda_time": 0.359295775945061, + "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": 2.4, + "pct_cuda_time": 0.03250564921095245, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.655, + "pct_cuda_time": 0.30683978453088656, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.473, + "pct_cuda_time": 0.019950342203222067, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 199.614, + "cuda_time_us": 16.544, + "pct_cuda_time": 0.2240722752274989, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.544, + "pct_cuda_time": 0.2240722752274989, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.574, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04074041367772707, + "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": 0.04074041367772707, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 454.371, + "cuda_time_us": 138.175, + "pct_cuda_time": 1.8714450332180645, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.736, + "cuda_time_us": 81.919, + "pct_cuda_time": 1.109512615713339, + "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": 81.919, + "pct_cuda_time": 1.109512615713339, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.184, + "cuda_time_us": 9.153, + "pct_cuda_time": 0.12396841967826991, + "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": 9.153, + "pct_cuda_time": 0.12396841967826991, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.151, + "cuda_time_us": 47.103, + "pct_cuda_time": 0.6379639978264555, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.639, + "pct_cuda_time": 0.6045915313032111, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033372466523244514, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2439.896, + "cuda_time_us": 211.80599999999998, + "pct_cuda_time": 2.8687048069895806, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.054, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.042474048302311204, + "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": 0.042474048302311204, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1772.138, + "cuda_time_us": 68.992, + "pct_cuda_time": 0.9344290626508465, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 149.472, + "cuda_time_us": 21.952, + "pct_cuda_time": 0.2973183381161784, + "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": 21.952, + "pct_cuda_time": 0.2973183381161784, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 523.582, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05200903873752392, + "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": 0.05200903873752392, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 759.695, + "cuda_time_us": 26.528, + "pct_cuda_time": 0.359295775945061, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.032939057867098484, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.592, + "pct_cuda_time": 0.30598651123909903, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.020370206838863536, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 178.66, + "cuda_time_us": 16.672, + "pct_cuda_time": 0.22580590985208301, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.672, + "pct_cuda_time": 0.22580590985208301, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.438, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04074041367772707, + "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": 0.04074041367772707, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 443.558, + "cuda_time_us": 136.67, + "pct_cuda_time": 1.8510612823586963, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 151.371, + "cuda_time_us": 81.375, + "pct_cuda_time": 1.1021446685588565, + "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": 81.375, + "pct_cuda_time": 1.1021446685588565, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.715, + "cuda_time_us": 9.216, + "pct_cuda_time": 0.12482169297005738, + "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": 0.12482169297005738, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 143.408, + "cuda_time_us": 46.079, + "pct_cuda_time": 0.6240949208297825, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.615, + "pct_cuda_time": 0.5907224543065379, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033372466523244514, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2319.155, + "cuda_time_us": 215.25900000000001, + "pct_cuda_time": 2.9154723097918387, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.575, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04204063964616517, + "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": 0.04204063964616517, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1653.11, + "cuda_time_us": 69.343, + "pct_cuda_time": 0.9391830138479482, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.226, + "cuda_time_us": 22.623, + "pct_cuda_time": 0.3064063758747405, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.623, + "pct_cuda_time": 0.3064063758747405, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 500.084, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.050275404112939785, + "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": 0.050275404112939785, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 692.912, + "cuda_time_us": 26.528, + "pct_cuda_time": 0.359295775945061, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.032939057867098484, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.624, + "pct_cuda_time": 0.30641991989524503, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.0199367981827175, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 172.737, + "cuda_time_us": 16.48, + "pct_cuda_time": 0.22320545791520682, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.48, + "pct_cuda_time": 0.22320545791520682, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.351, + "cuda_time_us": 3.231, + "pct_cuda_time": 0.04376073025024473, + "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": 3.231, + "pct_cuda_time": 0.04376073025024473, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 451.746, + "cuda_time_us": 139.58100000000002, + "pct_cuda_time": 1.8904879260474807, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 152.627, + "cuda_time_us": 82.943, + "pct_cuda_time": 1.123381692710012, + "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": 1.123381692710012, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 90.881, + "cuda_time_us": 9.343, + "pct_cuda_time": 0.12654178357413695, + "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": 9.343, + "pct_cuda_time": 0.12654178357413695, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.32, + "cuda_time_us": 47.295, + "pct_cuda_time": 0.6405644497633317, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.767, + "pct_cuda_time": 0.6063251659277952, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.03423928383553658, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2344.078, + "cuda_time_us": 211.711, + "pct_cuda_time": 2.8674181250416475, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 64.594, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04204063964616517, + "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": 0.04204063964616517, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1662.369, + "cuda_time_us": 68.608, + "pct_cuda_time": 0.9292281587770941, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.591, + "cuda_time_us": 21.728, + "pct_cuda_time": 0.2942844775231562, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.728, + "pct_cuda_time": 0.2942844775231562, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 463.603, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05157563008137789, + "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": 0.05157563008137789, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 698.031, + "cuda_time_us": 26.624, + "pct_cuda_time": 0.36059600191349916, + "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": 0.03380587517939055, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.656, + "pct_cuda_time": 0.3068533285513911, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.0199367981827175, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 195.475, + "cuda_time_us": 16.448, + "pct_cuda_time": 0.2227720492590608, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.448, + "pct_cuda_time": 0.2227720492590608, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.705, + "cuda_time_us": 2.816, + "pct_cuda_time": 0.03813996174085087, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.816, + "pct_cuda_time": 0.03813996174085087, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 467.607, + "cuda_time_us": 137.183, + "pct_cuda_time": 1.8580093648775373, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 145.713, + "cuda_time_us": 81.407, + "pct_cuda_time": 1.1025780772150022, + "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": 81.407, + "pct_cuda_time": 1.1025780772150022, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 91.999, + "cuda_time_us": 9.088, + "pct_cuda_time": 0.12308805834547326, + "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": 0.12308805834547326, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 175.693, + "cuda_time_us": 46.687999999999995, + "pct_cuda_time": 0.6323432293170616, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.224, + "pct_cuda_time": 0.598970762793817, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033372466523244514, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2392.136, + "cuda_time_us": 212.413, + "pct_cuda_time": 2.876926027435851, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.591, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04204063964616517, + "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": 0.04204063964616517, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1712.028, + "cuda_time_us": 68.702, + "pct_cuda_time": 0.930501296704523, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 161.317, + "cuda_time_us": 21.823, + "pct_cuda_time": 0.2955711594710897, + "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": 21.823, + "pct_cuda_time": 0.2955711594710897, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 516.156, + "cuda_time_us": 3.743, + "pct_cuda_time": 0.05069526874858125, + "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": 3.743, + "pct_cuda_time": 0.05069526874858125, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 703.121, + "cuda_time_us": 26.528000000000002, + "pct_cuda_time": 0.35929577594506107, + "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": 2.433, + "pct_cuda_time": 0.032952601887603045, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.655, + "pct_cuda_time": 0.30683978453088656, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.019503389526571466, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 185.009, + "cuda_time_us": 16.608, + "pct_cuda_time": 0.22493909253979094, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.608, + "pct_cuda_time": 0.22493909253979094, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.967, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04074041367772707, + "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": 0.04074041367772707, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 453.322, + "cuda_time_us": 137.59900000000002, + "pct_cuda_time": 1.8636436774074359, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 153.092, + "cuda_time_us": 82.239, + "pct_cuda_time": 1.1138467022747993, + "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": 82.239, + "pct_cuda_time": 1.1138467022747993, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 90.925, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.12178783237703518, + "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": 0.12178783237703518, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.777, + "cuda_time_us": 46.368, + "pct_cuda_time": 0.6280091427556013, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.872, + "pct_cuda_time": 0.5942032675762108, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.03380587517939055, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2395.813, + "cuda_time_us": 214.84699999999998, + "pct_cuda_time": 2.9098921733439584, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.453, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04334086561460327, + "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": 0.04334086561460327, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1713.392, + "cuda_time_us": 69.279, + "pct_cuda_time": 0.9383161965356561, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.412, + "cuda_time_us": 22.112, + "pct_cuda_time": 0.2994853813969085, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.112, + "pct_cuda_time": 0.2994853813969085, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 476.707, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.050275404112939785, + "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": 0.050275404112939785, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 725.845, + "cuda_time_us": 26.784, + "pct_cuda_time": 0.3627630451942293, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.032939057867098484, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.847, + "pct_cuda_time": 0.30944023646776275, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.020383750859368097, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 203.269, + "cuda_time_us": 16.671, + "pct_cuda_time": 0.22579236583157844, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.671, + "pct_cuda_time": 0.22579236583157844, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.298, + "cuda_time_us": 2.945, + "pct_cuda_time": 0.039887140385939567, + "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": 2.945, + "pct_cuda_time": 0.039887140385939567, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 452.024, + "cuda_time_us": 139.423, + "pct_cuda_time": 1.8883479708077597, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 154.215, + "cuda_time_us": 82.783, + "pct_cuda_time": 1.1212146494292818, + "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": 82.783, + "pct_cuda_time": 1.1212146494292818, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.205, + "cuda_time_us": 9.121, + "pct_cuda_time": 0.12353501102212387, + "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": 9.121, + "pct_cuda_time": 0.12353501102212387, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.92, + "cuda_time_us": 47.519, + "pct_cuda_time": 0.6435983103563538, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.415, + "pct_cuda_time": 0.6015576707101887, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04204063964616517, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2327.242, + "cuda_time_us": 211.902, + "pct_cuda_time": 2.870005032958019, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.784, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04334086561460327, + "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": 0.04334086561460327, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1662.492, + "cuda_time_us": 68.767, + "pct_cuda_time": 0.9313816580373194, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.338, + "cuda_time_us": 21.664, + "pct_cuda_time": 0.29341766021086413, + "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": 21.664, + "pct_cuda_time": 0.29341766021086413, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 503.584, + "cuda_time_us": 3.775, + "pct_cuda_time": 0.05112867740472729, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.775, + "pct_cuda_time": 0.05112867740472729, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 687.695, + "cuda_time_us": 26.689, + "pct_cuda_time": 0.3614763632462958, + "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": 2.625, + "pct_cuda_time": 0.035553053824479236, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.624, + "pct_cuda_time": 0.30641991989524503, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.019503389526571466, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 181.94, + "cuda_time_us": 16.639, + "pct_cuda_time": 0.2253589571754324, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.639, + "pct_cuda_time": 0.2253589571754324, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.166, + "cuda_time_us": 2.816, + "pct_cuda_time": 0.03813996174085087, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.816, + "pct_cuda_time": 0.03813996174085087, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 434.826, + "cuda_time_us": 137.119, + "pct_cuda_time": 1.8571425475652454, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 142.204, + "cuda_time_us": 81.823, + "pct_cuda_time": 1.1082123897449008, + "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": 81.823, + "pct_cuda_time": 1.1082123897449008, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 90.513, + "cuda_time_us": 9.088, + "pct_cuda_time": 0.12308805834547326, + "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": 0.12308805834547326, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.622, + "cuda_time_us": 46.208000000000006, + "pct_cuda_time": 0.6258420994748712, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.776, + "pct_cuda_time": 0.5929030416077727, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.032939057867098484, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2351.61, + "cuda_time_us": 212.254, + "pct_cuda_time": 2.874772528175625, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.87, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.042474048302311204, + "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": 0.042474048302311204, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1686.809, + "cuda_time_us": 68.224, + "pct_cuda_time": 0.9240272549033417, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 143.245, + "cuda_time_us": 21.664, + "pct_cuda_time": 0.29341766021086413, + "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": 21.664, + "pct_cuda_time": 0.29341766021086413, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 537.649, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05157563008137789, + "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": 0.05157563008137789, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 681.463, + "cuda_time_us": 26.368, + "pct_cuda_time": 0.3571287326643309, + "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": 2.4, + "pct_cuda_time": 0.03250564921095245, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.464, + "pct_cuda_time": 0.3042528766145149, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.020370206838863536, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 181.033, + "cuda_time_us": 16.384, + "pct_cuda_time": 0.2219052319467687, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.384, + "pct_cuda_time": 0.2219052319467687, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.683, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04074041367772707, + "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": 0.04074041367772707, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 446.544, + "cuda_time_us": 137.886, + "pct_cuda_time": 1.8675308112922455, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 151.59, + "cuda_time_us": 82.463, + "pct_cuda_time": 1.1168805628678213, + "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": 82.463, + "pct_cuda_time": 1.1168805628678213, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.469, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.12265464968932722, + "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": 0.12265464968932722, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.615, + "cuda_time_us": 46.367, + "pct_cuda_time": 0.6279955987350967, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.903, + "pct_cuda_time": 0.5946231322118521, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033372466523244514, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2486.282, + "cuda_time_us": 214.11, + "pct_cuda_time": 2.8999102302320954, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.262, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04334086561460327, + "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": 0.04334086561460327, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1766.84, + "cuda_time_us": 69.887, + "pct_cuda_time": 0.9465509610024307, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 185.629, + "cuda_time_us": 22.879, + "pct_cuda_time": 0.3098736451239088, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.879, + "pct_cuda_time": 0.3098736451239088, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 517.079, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.049841995456793756, + "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": 0.049841995456793756, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 740.504, + "cuda_time_us": 26.432, + "pct_cuda_time": 0.3579955499766229, + "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": 2.4, + "pct_cuda_time": 0.03250564921095245, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.56, + "pct_cuda_time": 0.30555310258295304, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.0199367981827175, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 179.335, + "cuda_time_us": 16.896, + "pct_cuda_time": 0.22883977044510523, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.896, + "pct_cuda_time": 0.22883977044510523, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.204, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.040307005021581035, + "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": 0.040307005021581035, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 503.578, + "cuda_time_us": 138.047, + "pct_cuda_time": 1.8697113985934801, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 151.652, + "cuda_time_us": 82.303, + "pct_cuda_time": 1.1147135195870914, + "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": 82.303, + "pct_cuda_time": 1.1147135195870914, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.292, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.12265464968932722, + "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": 0.12265464968932722, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 182.974, + "cuda_time_us": 46.687999999999995, + "pct_cuda_time": 0.6323432293170616, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.224, + "pct_cuda_time": 0.598970762793817, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033372466523244514, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2381.278, + "cuda_time_us": 213.054, + "pct_cuda_time": 2.8856077445792763, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.53, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.042474048302311204, + "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": 0.042474048302311204, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1712.272, + "cuda_time_us": 68.479, + "pct_cuda_time": 0.9274809801320052, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.442, + "cuda_time_us": 21.791, + "pct_cuda_time": 0.2951377508149437, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.791, + "pct_cuda_time": 0.2951377508149437, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 485.62, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.049841995456793756, + "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": 0.049841995456793756, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 723.986, + "cuda_time_us": 26.624, + "pct_cuda_time": 0.36059600191349916, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.032939057867098484, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.688, + "pct_cuda_time": 0.30728673720753713, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.020370206838863536, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 193.682, + "cuda_time_us": 16.384, + "pct_cuda_time": 0.2219052319467687, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.384, + "pct_cuda_time": 0.2219052319467687, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.738, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04074041367772707, + "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": 0.04074041367772707, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 448.023, + "cuda_time_us": 138.431, + "pct_cuda_time": 1.874912302467233, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 153.082, + "cuda_time_us": 82.687, + "pct_cuda_time": 1.1199144234608438, + "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": 1.1199144234608438, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 105.348, + "cuda_time_us": 9.248, + "pct_cuda_time": 0.12525510162620343, + "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": 0.12525510162620343, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 135.927, + "cuda_time_us": 46.496, + "pct_cuda_time": 0.6297427773801855, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.0, + "pct_cuda_time": 0.5959369022007949, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.03380587517939055, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2412.225, + "cuda_time_us": 213.118, + "pct_cuda_time": 2.886474561891568, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.342, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.042907456958457234, + "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": 0.042907456958457234, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1720.612, + "cuda_time_us": 68.704, + "pct_cuda_time": 0.930528384745532, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.231, + "cuda_time_us": 21.792, + "pct_cuda_time": 0.2951512948354483, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.792, + "pct_cuda_time": 0.2951512948354483, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 471.466, + "cuda_time_us": 3.968, + "pct_cuda_time": 0.05374267336210805, + "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": 3.968, + "pct_cuda_time": 0.05374267336210805, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 742.795, + "cuda_time_us": 26.4, + "pct_cuda_time": 0.3575621413204769, + "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": 2.4, + "pct_cuda_time": 0.03250564921095245, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.528, + "pct_cuda_time": 0.30511969392680693, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.0199367981827175, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 206.782, + "cuda_time_us": 16.544, + "pct_cuda_time": 0.2240722752274989, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.544, + "pct_cuda_time": 0.2240722752274989, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.554, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04074041367772707, + "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": 0.04074041367772707, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 467.927, + "cuda_time_us": 138.238, + "pct_cuda_time": 1.872298306509852, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 153.96, + "cuda_time_us": 81.918, + "pct_cuda_time": 1.1094990716928346, + "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": 81.918, + "pct_cuda_time": 1.1094990716928346, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.979, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.12438828431391136, + "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": 0.12438828431391136, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.628, + "cuda_time_us": 47.136, + "pct_cuda_time": 0.6384109505031061, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.639, + "pct_cuda_time": 0.6045915313032111, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.497, + "pct_cuda_time": 0.03381941919989511, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2373.625, + "cuda_time_us": 214.367, + "pct_cuda_time": 2.903391043501768, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.138, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.042474048302311204, + "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": 0.042474048302311204, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1703.713, + "cuda_time_us": 70.048, + "pct_cuda_time": 0.9487315483036656, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.624, + "cuda_time_us": 22.272, + "pct_cuda_time": 0.3016524246776387, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.272, + "pct_cuda_time": 0.3016524246776387, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 491.189, + "cuda_time_us": 3.681, + "pct_cuda_time": 0.04985553947729832, + "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": 0.04985553947729832, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 704.488, + "cuda_time_us": 27.295, + "pct_cuda_time": 0.36968403967206126, + "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": 2.591, + "pct_cuda_time": 0.035092557127324085, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 23.2, + "pct_cuda_time": 0.31422127570587366, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.020370206838863536, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 195.861, + "cuda_time_us": 16.8, + "pct_cuda_time": 0.22753954447666713, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.8, + "pct_cuda_time": 0.22753954447666713, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.678, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04160723099001914, + "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": 0.04160723099001914, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 449.042, + "cuda_time_us": 138.111, + "pct_cuda_time": 1.8705782159057722, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 153.371, + "cuda_time_us": 82.91, + "pct_cuda_time": 1.1229347400333614, + "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": 82.91, + "pct_cuda_time": 1.1229347400333614, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 92.566, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.12438828431391136, + "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": 0.12438828431391136, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 145.244, + "cuda_time_us": 46.017, + "pct_cuda_time": 0.6232551915584995, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.584, + "pct_cuda_time": 0.5903025896708965, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.433, + "pct_cuda_time": 0.032952601887603045, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2384.621, + "cuda_time_us": 214.589, + "pct_cuda_time": 2.906397816053781, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.538, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04204063964616517, + "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": 0.04204063964616517, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1706.068, + "cuda_time_us": 69.311, + "pct_cuda_time": 0.9387496051918023, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 173.966, + "cuda_time_us": 21.695, + "pct_cuda_time": 0.2938375248465056, + "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": 21.695, + "pct_cuda_time": 0.2938375248465056, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 474.875, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.05070881276908583, + "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": 0.05070881276908583, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 708.233, + "cuda_time_us": 26.976, + "pct_cuda_time": 0.3653634971311055, + "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": 2.4, + "pct_cuda_time": 0.03250564921095245, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.944, + "pct_cuda_time": 0.31075400645670537, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 1.632, + "pct_cuda_time": 0.022103841463447665, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 192.53, + "cuda_time_us": 16.896, + "pct_cuda_time": 0.22883977044510523, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.896, + "pct_cuda_time": 0.22883977044510523, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.424, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0411738223338731, + "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": 0.0411738223338731, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 448.318, + "cuda_time_us": 139.134, + "pct_cuda_time": 1.8844337488819405, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 148.741, + "cuda_time_us": 82.334, + "pct_cuda_time": 1.115133384222733, + "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": 82.334, + "pct_cuda_time": 1.115133384222733, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.307, + "cuda_time_us": 9.344, + "pct_cuda_time": 0.12655532759464153, + "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": 9.344, + "pct_cuda_time": 0.12655532759464153, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.471, + "cuda_time_us": 47.455999999999996, + "pct_cuda_time": 0.6427450370645664, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.928, + "pct_cuda_time": 0.6085057532290298, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.03423928383553658, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2429.353, + "cuda_time_us": 213.309, + "pct_cuda_time": 2.88906146980794, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.629, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.042907456958457234, + "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": 0.042907456958457234, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1690.397, + "cuda_time_us": 68.639, + "pct_cuda_time": 0.9296480234127354, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.36, + "cuda_time_us": 21.728, + "pct_cuda_time": 0.2942844775231562, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.728, + "pct_cuda_time": 0.2942844775231562, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 491.831, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.05070881276908583, + "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": 0.05070881276908583, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 730.522, + "cuda_time_us": 26.56, + "pct_cuda_time": 0.35972918460120706, + "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": 2.4, + "pct_cuda_time": 0.03250564921095245, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.656, + "pct_cuda_time": 0.3068533285513911, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.020370206838863536, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 176.541, + "cuda_time_us": 16.607, + "pct_cuda_time": 0.22492554851928637, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.607, + "pct_cuda_time": 0.22492554851928637, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 103.769, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0411738223338731, + "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": 0.0411738223338731, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.847, + "cuda_time_us": 138.462, + "pct_cuda_time": 1.875332167102874, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.481, + "cuda_time_us": 82.527, + "pct_cuda_time": 1.1177473801801137, + "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": 1.1177473801801137, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.705, + "cuda_time_us": 9.536, + "pct_cuda_time": 0.12915577953151772, + "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": 9.536, + "pct_cuda_time": 0.12915577953151772, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 147.957, + "cuda_time_us": 46.399, + "pct_cuda_time": 0.6284290073912427, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.903, + "pct_cuda_time": 0.5946231322118521, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.03380587517939055, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2259.539, + "cuda_time_us": 214.11200000000002, + "pct_cuda_time": 2.8999373182731047, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.887, + "cuda_time_us": 3.36, + "pct_cuda_time": 0.045507908895333425, + "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": 3.36, + "pct_cuda_time": 0.045507908895333425, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1613.621, + "cuda_time_us": 70.016, + "pct_cuda_time": 0.9482981396475194, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 143.841, + "cuda_time_us": 23.2, + "pct_cuda_time": 0.31422127570587366, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 23.2, + "pct_cuda_time": 0.31422127570587366, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 469.087, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.049841995456793756, + "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": 0.049841995456793756, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 687.827, + "cuda_time_us": 26.528, + "pct_cuda_time": 0.359295775945061, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.592, + "pct_cuda_time": 0.035106101147828646, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.496, + "pct_cuda_time": 0.30468628527066094, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.019503389526571466, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 169.835, + "cuda_time_us": 16.608, + "pct_cuda_time": 0.22493909253979094, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.608, + "pct_cuda_time": 0.22493909253979094, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.465, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04160723099001914, + "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": 0.04160723099001914, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 430.809, + "cuda_time_us": 137.66400000000002, + "pct_cuda_time": 1.8645240387402326, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 141.752, + "cuda_time_us": 82.239, + "pct_cuda_time": 1.1138467022747993, + "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": 82.239, + "pct_cuda_time": 1.1138467022747993, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.332, + "cuda_time_us": 9.248, + "pct_cuda_time": 0.12525510162620343, + "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": 0.12525510162620343, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.004, + "cuda_time_us": 46.17700000000001, + "pct_cuda_time": 0.6254222348392298, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.712, + "pct_cuda_time": 0.5920362242954806, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.465, + "pct_cuda_time": 0.033386010543749074, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2269.529, + "cuda_time_us": 214.494, + "pct_cuda_time": 2.905111134105848, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.423, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.042907456958457234, + "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": 0.042907456958457234, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1589.608, + "cuda_time_us": 69.664, + "pct_cuda_time": 0.9435306444299132, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.818, + "cuda_time_us": 22.591, + "pct_cuda_time": 0.3059729672185945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.591, + "pct_cuda_time": 0.3059729672185945, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 446.467, + "cuda_time_us": 3.681, + "pct_cuda_time": 0.04985553947729832, + "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": 0.04985553947729832, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 685.732, + "cuda_time_us": 26.72, + "pct_cuda_time": 0.36189622788193726, + "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": 2.4, + "pct_cuda_time": 0.03250564921095245, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.816, + "pct_cuda_time": 0.3090203718321213, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.020370206838863536, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 178.274, + "cuda_time_us": 16.672, + "pct_cuda_time": 0.22580590985208301, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.672, + "pct_cuda_time": 0.22580590985208301, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.97, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04074041367772707, + "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": 0.04074041367772707, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 442.891, + "cuda_time_us": 138.654, + "pct_cuda_time": 1.8779326190397503, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 148.185, + "cuda_time_us": 81.791, + "pct_cuda_time": 1.1077789810887548, + "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": 81.791, + "pct_cuda_time": 1.1077789810887548, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.942, + "cuda_time_us": 9.536, + "pct_cuda_time": 0.12915577953151772, + "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": 9.536, + "pct_cuda_time": 0.12915577953151772, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 145.124, + "cuda_time_us": 47.327, + "pct_cuda_time": 0.6409978584194777, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.415, + "pct_cuda_time": 0.6015576707101887, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.912, + "pct_cuda_time": 0.03944018770928897, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2417.101, + "cuda_time_us": 212.47500000000002, + "pct_cuda_time": 2.8777657567071344, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.598, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.042907456958457234, + "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": 0.042907456958457234, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1752.759, + "cuda_time_us": 68.542, + "pct_cuda_time": 0.9283342534237928, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.524, + "cuda_time_us": 21.823, + "pct_cuda_time": 0.2955711594710897, + "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": 21.823, + "pct_cuda_time": 0.2955711594710897, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 507.385, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.051142221425231844, + "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": 0.051142221425231844, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 766.622, + "cuda_time_us": 26.431, + "pct_cuda_time": 0.3579820059561184, + "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": 2.367, + "pct_cuda_time": 0.03205869653430185, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.592, + "pct_cuda_time": 0.30598651123909903, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.0199367981827175, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 181.579, + "cuda_time_us": 16.512, + "pct_cuda_time": 0.22363886657135285, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.512, + "pct_cuda_time": 0.22363886657135285, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.944, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04074041367772707, + "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": 0.04074041367772707, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 431.416, + "cuda_time_us": 137.757, + "pct_cuda_time": 1.8657836326471569, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 144.332, + "cuda_time_us": 82.239, + "pct_cuda_time": 1.1138467022747993, + "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": 82.239, + "pct_cuda_time": 1.1138467022747993, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.827, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.12135442372088914, + "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": 0.12135442372088914, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.176, + "cuda_time_us": 46.558, + "pct_cuda_time": 0.6305825066514683, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.871, + "pct_cuda_time": 0.5941897235557062, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.687, + "pct_cuda_time": 0.03639278309576218, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2378.649, + "cuda_time_us": 213.309, + "pct_cuda_time": 2.88906146980794, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.731, + "cuda_time_us": 3.167, + "pct_cuda_time": 0.042893912937952666, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.167, + "pct_cuda_time": 0.042893912937952666, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1708.352, + "cuda_time_us": 69.056, + "pct_cuda_time": 0.9352958799631383, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 141.332, + "cuda_time_us": 22.272, + "pct_cuda_time": 0.3016524246776387, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.272, + "pct_cuda_time": 0.3016524246776387, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 481.991, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.049841995456793756, + "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": 0.049841995456793756, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 740.16, + "cuda_time_us": 26.464, + "pct_cuda_time": 0.35842895863276897, + "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": 2.4, + "pct_cuda_time": 0.03250564921095245, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.56, + "pct_cuda_time": 0.30555310258295304, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.020370206838863536, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 173.579, + "cuda_time_us": 16.64, + "pct_cuda_time": 0.225372501195937, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.64, + "pct_cuda_time": 0.225372501195937, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.416, + "cuda_time_us": 2.944, + "pct_cuda_time": 0.039873596365435, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.944, + "pct_cuda_time": 0.039873596365435, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 444.239, + "cuda_time_us": 138.142, + "pct_cuda_time": 1.8709980805414135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 147.732, + "cuda_time_us": 82.815, + "pct_cuda_time": 1.1216480580854278, + "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": 1.1216480580854278, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.477, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.12178783237703518, + "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": 0.12178783237703518, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.12, + "cuda_time_us": 46.335, + "pct_cuda_time": 0.6275621900789506, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.871, + "pct_cuda_time": 0.5941897235557062, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.033372466523244514, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2429.132, + "cuda_time_us": 213.948, + "pct_cuda_time": 2.8977160989103563, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 64.691, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.042907456958457234, + "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": 0.042907456958457234, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1712.853, + "cuda_time_us": 69.311, + "pct_cuda_time": 0.9387496051918023, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 140.054, + "cuda_time_us": 21.568, + "pct_cuda_time": 0.29211743424242603, + "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": 21.568, + "pct_cuda_time": 0.29211743424242603, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 472.89, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05157563008137789, + "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": 0.05157563008137789, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 744.455, + "cuda_time_us": 26.591, + "pct_cuda_time": 0.36014904923684854, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.56, + "pct_cuda_time": 0.03467269249168261, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.559, + "pct_cuda_time": 0.30553955856244847, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.0199367981827175, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 198.818, + "cuda_time_us": 17.344, + "pct_cuda_time": 0.2349074916311497, + "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": 17.344, + "pct_cuda_time": 0.2349074916311497, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 108.385, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04160723099001914, + "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": 0.04160723099001914, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 458.422, + "cuda_time_us": 138.397, + "pct_cuda_time": 1.8744518057700772, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 149.799, + "cuda_time_us": 82.238, + "pct_cuda_time": 1.1138331582542949, + "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": 82.238, + "pct_cuda_time": 1.1138331582542949, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.879, + "cuda_time_us": 9.344, + "pct_cuda_time": 0.12655532759464153, + "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": 9.344, + "pct_cuda_time": 0.12655532759464153, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 143.85, + "cuda_time_us": 46.815, + "pct_cuda_time": 0.6340633199211412, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.352, + "pct_cuda_time": 0.6007043974184012, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.463, + "pct_cuda_time": 0.03335892250273995, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2411.737, + "cuda_time_us": 213.11700000000002, + "pct_cuda_time": 2.886461017871064, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.538, + "cuda_time_us": 3.392, + "pct_cuda_time": 0.045941317551479455, + "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": 3.392, + "pct_cuda_time": 0.045941317551479455, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1716.384, + "cuda_time_us": 68.415, + "pct_cuda_time": 0.9266141628197133, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.298, + "cuda_time_us": 21.631, + "pct_cuda_time": 0.2929707075342135, + "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": 21.631, + "pct_cuda_time": 0.2929707075342135, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 490.975, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.050275404112939785, + "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": 0.050275404112939785, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 732.6, + "cuda_time_us": 26.72, + "pct_cuda_time": 0.36189622788193726, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.032939057867098484, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.816, + "pct_cuda_time": 0.3090203718321213, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.0199367981827175, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 177.257, + "cuda_time_us": 16.352, + "pct_cuda_time": 0.2214718232906227, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.352, + "pct_cuda_time": 0.2214718232906227, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.397, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04074041367772707, + "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": 0.04074041367772707, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 461.801, + "cuda_time_us": 138.30200000000002, + "pct_cuda_time": 1.873165123822144, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 154.256, + "cuda_time_us": 82.111, + "pct_cuda_time": 1.1121130676502153, + "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": 82.111, + "pct_cuda_time": 1.1121130676502153, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.555, + "cuda_time_us": 9.792, + "pct_cuda_time": 0.13262304878068598, + "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": 9.792, + "pct_cuda_time": 0.13262304878068598, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.99, + "cuda_time_us": 46.399, + "pct_cuda_time": 0.6284290073912427, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 43.839, + "pct_cuda_time": 0.5937563148995602, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.56, + "pct_cuda_time": 0.03467269249168261, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2388.564, + "cuda_time_us": 215.133, + "pct_cuda_time": 2.913765763208264, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.12, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04202709562566061, + "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": 0.04202709562566061, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1710.558, + "cuda_time_us": 70.335, + "pct_cuda_time": 0.952618682188475, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.769, + "cuda_time_us": 23.008, + "pct_cuda_time": 0.31162082376899747, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 23.008, + "pct_cuda_time": 0.31162082376899747, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[24, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 480.223, + "cuda_time_us": 3.711, + "pct_cuda_time": 0.05026186009243522, + "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": 3.711, + "pct_cuda_time": 0.05026186009243522, + "trace": "_C::rotary_embedding(int64[24], bfloat16[24, 4096], bfloat16[24, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 744.395, + "cuda_time_us": 26.848, + "pct_cuda_time": 0.36362986250652135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.032939057867098484, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[24], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 22.976, + "pct_cuda_time": 0.3111874151128514, + "trace": "_vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 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": 0.019503389526571466, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[24, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[24, 1, 32, 128], None, None, None, None, int32[24], None, None, int32[24, 17], 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[24, 32, 128], bfloat16[24, 8, 128], bfloat16[24, 8, 128], bfloat16[24, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 185.857, + "cuda_time_us": 16.768, + "pct_cuda_time": 0.2271061358205211, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.768, + "pct_cuda_time": 0.2271061358205211, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[24, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.154, + "cuda_time_us": 2.817, + "pct_cuda_time": 0.03815350576135544, + "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": 2.817, + "pct_cuda_time": 0.03815350576135544, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 445.461, + "cuda_time_us": 138.87800000000001, + "pct_cuda_time": 1.8809664796327725, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 146.747, + "cuda_time_us": 82.623, + "pct_cuda_time": 1.1190476061485517, + "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": 82.623, + "pct_cuda_time": 1.1190476061485517, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[24, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.509, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.1222212410331812, + "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": 0.1222212410331812, + "trace": "_C::silu_and_mul(bfloat16[24, 14336], bfloat16[24, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.454, + "cuda_time_us": 47.231, + "pct_cuda_time": 0.6396976324510396, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 44.575, + "pct_cuda_time": 0.6037247139909189, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.656, + "pct_cuda_time": 0.03597291846012071, + "trace": "mm(bfloat16[24, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[24, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[24, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.273, + "cuda_time_us": 3.328, + "pct_cuda_time": 0.045074500239187396, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.328, + "pct_cuda_time": 0.045074500239187396, + "trace": "_C::fused_add_rms_norm(bfloat16[24, 4096], bfloat16[24, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 657.776, + "cuda_time_us": 390.908, + "pct_cuda_time": 5.294465967397917, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 3.552, + "pct_cuda_time": 0.04810836083220962, + "trace": "index_select(bfloat16[24, 4096], 0, int64[24])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.769, + "pct_cuda_time": 0.010415351768009347, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[24, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 386.587, + "pct_cuda_time": 5.235942254797697, + "trace": "mm(bfloat16[24, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[24, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[24, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 4842.935, + "cuda_time_us": 143.966, + "pct_cuda_time": 1.9498784559599918, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.00996839909135875, + "trace": "copy_(bfloat16[24], bfloat16[24], True) <- _to_copy(bfloat16[24], 15, 0, None, None, True, None) <- to(bfloat16[24], 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": 0.00996839909135875, + "trace": "copy_(bfloat16[24], bfloat16[24], True) <- _to_copy(bfloat16[24], 15, 0, None, None, True, None) <- to(bfloat16[24], 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": 0.00996839909135875, + "trace": "copy_(int32[24], int32[24], True) <- _to_copy(int32[24], 3, 0, None, None, True, None) <- to(int32[24], 3, 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": 0.00996839909135875, + "trace": "copy_(bfloat16[24], bfloat16[24], True) <- _to_copy(bfloat16[24], 15, 0, None, None, True, None) <- to(bfloat16[24], 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": 0.010401807747504785, + "trace": "copy_(bfloat16[24], bfloat16[24], True) <- _to_copy(bfloat16[24], 15, 0, None, None, True, None) <- to(bfloat16[24], 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": 0.00996839909135875, + "trace": "copy_(bfloat16[24], bfloat16[24], True) <- _to_copy(bfloat16[24], 15, 0, None, None, True, None) <- to(bfloat16[24], 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": 0.00996839909135875, + "trace": "copy_(bfloat16[24], bfloat16[24], True) <- _to_copy(bfloat16[24], 15, 0, None, None, True, None) <- to(bfloat16[24], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 9.888, + "pct_cuda_time": 0.13392327474912408, + "trace": "copy_(float32[24, 128256], bfloat16[24, 128256], False) <- _to_copy(bfloat16[24, 128256], 6, None, None, None, False, None) <- to(bfloat16[24, 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": 15.36, + "pct_cuda_time": 0.20803615495009567, + "trace": "div_(float32[24, 128256], bfloat16[24, 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": 35.455, + "pct_cuda_time": 0.48020324698929956, + "trace": "_softmax(float32[24, 128256], -1, False) <- softmax(float32[24, 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": 30.591, + "pct_cuda_time": 0.4143251312551026, + "trace": "_log_softmax(float32[24, 128256], -1, False) <- log_softmax(float32[24, 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": 1.888, + "pct_cuda_time": 0.025571110712615922, + "trace": "copy_(int64[24], int32[24], False) <- _to_copy(int32[24], 4, None, None, None, False, None) <- to(int32[24], 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": 15.52, + "pct_cuda_time": 0.2102031982308258, + "trace": "index(float32[24, 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": 27.584, + "pct_cuda_time": 0.3735982615978801, + "trace": "argmax(float32[24, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.03380587517939055, + "trace": "copy_(int64[24], int64[24], False) <- _to_copy(int64[24], 4, 0, None, None, False, None) <- to(int64[24], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + } +} \ No newline at end of file