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"RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 902.095, + "cuda_time_us": 48.351, + "pct_cuda_time": 0.07318951540324369, + "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": 48.351, + "pct_cuda_time": 0.07318951540324369, + "trace": "_C::rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 3113.32, + "cuda_time_us": 460.09, + "pct_cuda_time": 0.6964440061607492, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 434.113, + "cuda_time_us": 210.397, + "pct_cuda_time": 0.31848057893934484, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 209.661, + "pct_cuda_time": 0.31736648650409455, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 998.988, + "cuda_time_us": 40.223, + "pct_cuda_time": 0.06088605981395774, + "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": 40.223, + "pct_cuda_time": 0.06088605981395774, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1075.448, + "cuda_time_us": 63.038999999999994, + "pct_cuda_time": 0.0954229253067171, + "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": 15.808, + "pct_cuda_time": 0.023928767957115182, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.791, + "pct_cuda_time": 0.06931441128063394, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.002179746068967982, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 316.639, + "cuda_time_us": 146.43099999999998, + "pct_cuda_time": 0.2216544421007296, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011156061477981963, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 145.694, + "pct_cuda_time": 0.2205388359529314, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 128.075, + "cuda_time_us": 30.272, + "pct_cuda_time": 0.045823106249860246, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.272, + "pct_cuda_time": 0.045823106249860246, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 597.964, + "cuda_time_us": 1514.1879999999999, + "pct_cuda_time": 2.292045375471174, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 195.699, + "cuda_time_us": 947.7959999999999, + "pct_cuda_time": 1.434690698044151, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 947.06, + "pct_cuda_time": 1.4335766056089008, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 148.222, + "cuda_time_us": 130.654, + "pct_cuda_time": 0.19777259923259913, + "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": 130.654, + "pct_cuda_time": 0.19777259923259913, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 174.651, + "cuda_time_us": 435.738, + "pct_cuda_time": 0.659582078194424, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 435.002, + "pct_cuda_time": 0.6584679857591738, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2764.275, + "cuda_time_us": 2036.2290000000003, + "pct_cuda_time": 3.0822653876865322, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.531, + "cuda_time_us": 33.44, + "pct_cuda_time": 0.050618547601589806, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.44, + "pct_cuda_time": 0.050618547601589806, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2009.247, + "cuda_time_us": 456.47299999999996, + "pct_cuda_time": 0.6909689078750151, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 166.99, + "cuda_time_us": 206.877, + "pct_cuda_time": 0.31315231077075645, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 206.109, + "pct_cuda_time": 0.31198977953397355, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 578.111, + "cuda_time_us": 40.512, + "pct_cuda_time": 0.06132352274029923, + "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": 40.512, + "pct_cuda_time": 0.06132352274029923, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 858.287, + "cuda_time_us": 62.91, + "pct_cuda_time": 0.09522765638803872, + "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": 15.903, + "pct_cuda_time": 0.024072570649165154, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.727, + "pct_cuda_time": 0.0692175336775687, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.001937552061304873, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 216.185, + "cuda_time_us": 146.17399999999998, + "pct_cuda_time": 0.22126541797592067, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 145.438, + "pct_cuda_time": 0.2201513255406704, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.219, + "cuda_time_us": 30.88, + "pct_cuda_time": 0.04674344347898006, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.88, + "pct_cuda_time": 0.04674344347898006, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 492.478, + "cuda_time_us": 1515.4360000000001, + "pct_cuda_time": 2.293934488730947, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 162.821, + "cuda_time_us": 948.34, + "pct_cuda_time": 1.4355141576702057, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 947.604, + "pct_cuda_time": 1.4344000652349556, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 111.528, + "cuda_time_us": 131.166, + "pct_cuda_time": 0.19854762005712107, + "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": 131.166, + "pct_cuda_time": 0.19854762005712107, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 157.223, + "cuda_time_us": 435.93, + "pct_cuda_time": 0.6598727110036199, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 435.194, + "pct_cuda_time": 0.6587586185683695, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2671.975, + "cuda_time_us": 2037.2509999999997, + "pct_cuda_time": 3.083812401910479, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.427, + "cuda_time_us": 32.959, + "pct_cuda_time": 0.0498904518660526, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.959, + "pct_cuda_time": 0.0498904518660526, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1883.607, + "cuda_time_us": 456.633, + "pct_cuda_time": 0.6912111018826782, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 161.523, + "cuda_time_us": 206.71699999999998, + "pct_cuda_time": 0.3129101167630933, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 205.981, + "pct_cuda_time": 0.31179602432784304, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 539.45, + "cuda_time_us": 39.967, + "pct_cuda_time": 0.06049854940169677, + "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": 39.967, + "pct_cuda_time": 0.06049854940169677, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 784.328, + "cuda_time_us": 63.007, + "pct_cuda_time": 0.09537448650518449, + "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": 15.712, + "pct_cuda_time": 0.023783451552517317, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.951, + "pct_cuda_time": 0.06955660528829705, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 1.344, + "pct_cuda_time": 0.002034429664370117, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 234.539, + "cuda_time_us": 146.942, + "pct_cuda_time": 0.22242794921270367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 146.174, + "pct_cuda_time": 0.22126541797592073, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.411, + "cuda_time_us": 30.271, + "pct_cuda_time": 0.045821592537312356, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.271, + "pct_cuda_time": 0.045821592537312356, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 536.429, + "cuda_time_us": 1517.388, + "pct_cuda_time": 2.2968892556244365, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 178.211, + "cuda_time_us": 950.5160000000001, + "pct_cuda_time": 1.4388079961744242, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 949.748, + "pct_cuda_time": 1.4376454649376413, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 128.68, + "cuda_time_us": 130.942, + "pct_cuda_time": 0.19820854844639274, + "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": 130.942, + "pct_cuda_time": 0.19820854844639274, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 162.974, + "cuda_time_us": 435.92999999999995, + "pct_cuda_time": 0.6598727110036198, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 435.162, + "pct_cuda_time": 0.6587101797668369, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2545.878, + "cuda_time_us": 2029.092, + "pct_cuda_time": 3.0714620212322092, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.177, + "cuda_time_us": 32.736, + "pct_cuda_time": 0.04955289396787213, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.736, + "pct_cuda_time": 0.04955289396787213, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1817.874, + "cuda_time_us": 453.817, + "pct_cuda_time": 0.6869484873478074, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.451, + "cuda_time_us": 206.26899999999998, + "pct_cuda_time": 0.3122319735416366, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 205.533, + "pct_cuda_time": 0.3111178811063863, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 503.845, + "cuda_time_us": 39.968, + "pct_cuda_time": 0.06050006311424466, + "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": 39.968, + "pct_cuda_time": 0.06050006311424466, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 795.595, + "cuda_time_us": 62.942, + "pct_cuda_time": 0.09527609518957135, + "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": 15.615, + "pct_cuda_time": 0.02363662143537156, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.855, + "pct_cuda_time": 0.06941128888369918, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.002228184870500604, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 205.28, + "cuda_time_us": 144.638, + "pct_cuda_time": 0.21894035550235488, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 143.87, + "pct_cuda_time": 0.21777782426557196, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 88.682, + "cuda_time_us": 30.432, + "pct_cuda_time": 0.04606530025752336, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.432, + "pct_cuda_time": 0.04606530025752336, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 487.852, + "cuda_time_us": 1512.107, + "pct_cuda_time": 2.288895339659006, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.942, + "cuda_time_us": 947.219, + "pct_cuda_time": 1.4338172859040161, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0011125787227024076, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 946.484, + "pct_cuda_time": 1.4327047071813137, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 107.726, + "cuda_time_us": 129.886, + "pct_cuda_time": 0.1966100679958162, + "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": 129.886, + "pct_cuda_time": 0.1966100679958162, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 160.238, + "cuda_time_us": 435.00199999999995, + "pct_cuda_time": 0.6584679857591736, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 434.234, + "pct_cuda_time": 0.6573054545223908, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2662.094, + "cuda_time_us": 2030.246, + "pct_cuda_time": 3.0732088455124793, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.14, + "cuda_time_us": 32.448, + "pct_cuda_time": 0.04911694475407853, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.448, + "pct_cuda_time": 0.04911694475407853, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1890.995, + "cuda_time_us": 453.40099999999995, + "pct_cuda_time": 0.6863187829278834, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 157.736, + "cuda_time_us": 205.18099999999998, + "pct_cuda_time": 0.3105850542895275, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 204.445, + "pct_cuda_time": 0.30947096185427714, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 513.277, + "cuda_time_us": 39.775, + "pct_cuda_time": 0.060207916592501035, + "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": 39.775, + "pct_cuda_time": 0.060207916592501035, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 826.53, + "cuda_time_us": 63.422, + "pct_cuda_time": 0.09600267721256067, + "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": 15.712, + "pct_cuda_time": 0.023783451552517317, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 46.239, + "pct_cuda_time": 0.06999255450209065, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.0022266711579527095, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 226.116, + "cuda_time_us": 145.023, + "pct_cuda_time": 0.21952313483329422, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011156061477981963, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 144.286, + "pct_cuda_time": 0.21840752868549607, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 90.057, + "cuda_time_us": 30.528, + "pct_cuda_time": 0.04621061666212122, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.528, + "pct_cuda_time": 0.04621061666212122, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 528.533, + "cuda_time_us": 1513.8690000000001, + "pct_cuda_time": 2.291562501168396, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 171.293, + "cuda_time_us": 948.403, + "pct_cuda_time": 1.4356095215607232, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 947.667, + "pct_cuda_time": 1.4344954291254728, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 111.997, + "cuda_time_us": 130.239, + "pct_cuda_time": 0.19714440852522294, + "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": 130.239, + "pct_cuda_time": 0.19714440852522294, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 169.26, + "cuda_time_us": 435.22700000000003, + "pct_cuda_time": 0.6588085710824501, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011156061477981963, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 434.49, + "pct_cuda_time": 0.6576929649346518, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2859.076, + "cuda_time_us": 2033.481, + "pct_cuda_time": 3.0781057056049175, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.878, + "cuda_time_us": 32.736, + "pct_cuda_time": 0.04955289396787213, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.736, + "pct_cuda_time": 0.04955289396787213, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2060.862, + "cuda_time_us": 453.81999999999994, + "pct_cuda_time": 0.6869530284854511, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.64, + "cuda_time_us": 205.599, + "pct_cuda_time": 0.31121778613454737, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011156061477981963, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 204.862, + "pct_cuda_time": 0.31010217998674916, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 707.926, + "cuda_time_us": 40.479, + "pct_cuda_time": 0.06127357022621872, + "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": 40.479, + "pct_cuda_time": 0.06127357022621872, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 790.222, + "cuda_time_us": 62.206999999999994, + "pct_cuda_time": 0.09416351646686894, + "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": 15.647, + "pct_cuda_time": 0.02368506023690418, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.312, + "pct_cuda_time": 0.06858934297019251, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.0018891132597722512, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 215.294, + "cuda_time_us": 145.535, + "pct_cuda_time": 0.22029815565781619, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 144.767, + "pct_cuda_time": 0.21913562442103324, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.179, + "cuda_time_us": 30.592, + "pct_cuda_time": 0.04630749426518647, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.592, + "pct_cuda_time": 0.04630749426518647, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 530.886, + "cuda_time_us": 1516.333, + "pct_cuda_time": 2.295292288886408, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 205.514, + "cuda_time_us": 951.284, + "pct_cuda_time": 1.4399705274112071, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 950.548, + "pct_cuda_time": 1.4388564349759567, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.441, + "cuda_time_us": 130.175, + "pct_cuda_time": 0.19704753092215774, + "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": 130.175, + "pct_cuda_time": 0.19704753092215774, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 161.856, + "cuda_time_us": 434.87399999999997, + "pct_cuda_time": 0.6582742305530432, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 434.138, + "pct_cuda_time": 0.657160138117793, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2596.727, + "cuda_time_us": 2032.071, + "pct_cuda_time": 3.0759713709123866, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.065, + "cuda_time_us": 33.216, + "pct_cuda_time": 0.050279475990861466, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.216, + "pct_cuda_time": 0.050279475990861466, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1883.802, + "cuda_time_us": 454.10499999999996, + "pct_cuda_time": 0.687384436561601, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 168.169, + "cuda_time_us": 205.469, + "pct_cuda_time": 0.31102100350332107, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 204.733, + "pct_cuda_time": 0.3099069110680708, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 537.05, + "cuda_time_us": 39.935, + "pct_cuda_time": 0.06045011060016415, + "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": 39.935, + "pct_cuda_time": 0.06045011060016415, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 798.452, + "cuda_time_us": 62.687999999999995, + "pct_cuda_time": 0.09489161220240615, + "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": 15.648, + "pct_cuda_time": 0.023686573949452075, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.696, + "pct_cuda_time": 0.06917060858858397, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 1.344, + "pct_cuda_time": 0.002034429664370117, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 207.507, + "cuda_time_us": 146.01299999999998, + "pct_cuda_time": 0.22102171025570969, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 145.277, + "pct_cuda_time": 0.21990761782045937, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.597, + "cuda_time_us": 30.463, + "pct_cuda_time": 0.046112225346508086, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.463, + "pct_cuda_time": 0.046112225346508086, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 475.833, + "cuda_time_us": 1514.287, + "pct_cuda_time": 2.2921952330134157, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.239, + "cuda_time_us": 949.62, + "pct_cuda_time": 1.4374517097315105, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 948.884, + "pct_cuda_time": 1.4363376172962603, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.159, + "cuda_time_us": 130.207, + "pct_cuda_time": 0.1970959697236903, + "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": 130.207, + "pct_cuda_time": 0.1970959697236903, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.323, + "cuda_time_us": 434.46000000000004, + "pct_cuda_time": 0.657647553558215, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011156061477981963, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 433.723, + "pct_cuda_time": 0.6565319474104169, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2484.965, + "cuda_time_us": 2030.1819999999998, + "pct_cuda_time": 3.0731119679094134, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.594, + "cuda_time_us": 32.703, + "pct_cuda_time": 0.04950294145379162, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.703, + "pct_cuda_time": 0.04950294145379162, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1776.598, + "cuda_time_us": 452.378, + "pct_cuda_time": 0.6847702549913873, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.704, + "cuda_time_us": 205.278, + "pct_cuda_time": 0.3107318844066732, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 204.51, + "pct_cuda_time": 0.30956935316989026, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 481.135, + "cuda_time_us": 39.647, + "pct_cuda_time": 0.06001416138637055, + "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": 39.647, + "pct_cuda_time": 0.06001416138637055, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 771.004, + "cuda_time_us": 62.431, + "pct_cuda_time": 0.09450258807759729, + "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": 15.968, + "pct_cuda_time": 0.024170961964778293, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.023, + "pct_cuda_time": 0.06815188004385102, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.002179746068967982, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 196.429, + "cuda_time_us": 145.022, + "pct_cuda_time": 0.21952162112074633, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 144.286, + "pct_cuda_time": 0.21840752868549607, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.97, + "cuda_time_us": 30.944, + "pct_cuda_time": 0.046840321082045305, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.944, + "pct_cuda_time": 0.046840321082045305, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 481.346, + "cuda_time_us": 1514.157, + "pct_cuda_time": 2.2919984503821893, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 161.684, + "cuda_time_us": 948.212, + "pct_cuda_time": 1.4353204024640753, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 947.476, + "pct_cuda_time": 1.434206310028825, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.147, + "cuda_time_us": 129.887, + "pct_cuda_time": 0.19661158170836412, + "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": 129.887, + "pct_cuda_time": 0.19661158170836412, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 157.588, + "cuda_time_us": 436.058, + "pct_cuda_time": 0.6600664662097503, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 435.322, + "pct_cuda_time": 0.6589523737745, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2651.613, + "cuda_time_us": 2036.452, + "pct_cuda_time": 3.0826029455847124, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.842, + "cuda_time_us": 32.576, + "pct_cuda_time": 0.04931069996020902, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.576, + "pct_cuda_time": 0.04931069996020902, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1793.65, + "cuda_time_us": 455.22400000000005, + "pct_cuda_time": 0.6890782809026951, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 185.862, + "cuda_time_us": 206.461, + "pct_cuda_time": 0.31252260635083234, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 205.693, + "pct_cuda_time": 0.31136007511404945, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 498.224, + "cuda_time_us": 39.935, + "pct_cuda_time": 0.06045011060016415, + "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": 39.935, + "pct_cuda_time": 0.06045011060016415, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 758.131, + "cuda_time_us": 63.391, + "pct_cuda_time": 0.09595575212357595, + "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": 15.84, + "pct_cuda_time": 0.023977206758647805, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 46.079, + "pct_cuda_time": 0.06975036049442754, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.002228184870500604, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 202.725, + "cuda_time_us": 145.437, + "pct_cuda_time": 0.22014981182812257, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0011125787227024076, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 144.702, + "pct_cuda_time": 0.21903723310542014, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.196, + "cuda_time_us": 31.488, + "pct_cuda_time": 0.04766378070809988, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.488, + "pct_cuda_time": 0.04766378070809988, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 626.334, + "cuda_time_us": 1517.164, + "pct_cuda_time": 2.296550184013708, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 162.757, + "cuda_time_us": 952.084, + "pct_cuda_time": 1.4411814974495225, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 951.348, + "pct_cuda_time": 1.440067405014272, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.726, + "cuda_time_us": 130.11, + "pct_cuda_time": 0.1969491396065446, + "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": 130.11, + "pct_cuda_time": 0.1969491396065446, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 241.224, + "cuda_time_us": 434.96999999999997, + "pct_cuda_time": 0.658419546957641, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 434.234, + "pct_cuda_time": 0.6573054545223908, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2536.563, + "cuda_time_us": 2031.1750000000002, + "pct_cuda_time": 3.0746150844694733, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.662, + "cuda_time_us": 33.632, + "pct_cuda_time": 0.05090918041078554, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.632, + "pct_cuda_time": 0.05090918041078554, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1830.846, + "cuda_time_us": 453.563, + "pct_cuda_time": 0.6865640043606424, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 157.888, + "cuda_time_us": 205.053, + "pct_cuda_time": 0.31039129908339697, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 204.317, + "pct_cuda_time": 0.30927720664814673, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 530.479, + "cuda_time_us": 39.968, + "pct_cuda_time": 0.06050006311424466, + "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": 39.968, + "pct_cuda_time": 0.06050006311424466, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 786.803, + "cuda_time_us": 62.399, + "pct_cuda_time": 0.09445414927606467, + "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": 15.519, + "pct_cuda_time": 0.023491305030773693, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.6, + "pct_cuda_time": 0.0690252921839861, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.001937552061304873, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 193.71, + "cuda_time_us": 146.143, + "pct_cuda_time": 0.22121849288693599, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 145.407, + "pct_cuda_time": 0.2201044004516857, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.587, + "cuda_time_us": 29.984, + "pct_cuda_time": 0.04538715703606666, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 29.984, + "pct_cuda_time": 0.04538715703606666, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 469.526, + "cuda_time_us": 1513.996, + "pct_cuda_time": 2.2917547426619786, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 154.974, + "cuda_time_us": 948.724, + "pct_cuda_time": 1.4360954232885974, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 947.988, + "pct_cuda_time": 1.434981330853347, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.09, + "cuda_time_us": 129.79, + "pct_cuda_time": 0.19646475159121832, + "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": 129.79, + "pct_cuda_time": 0.19646475159121832, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.811, + "cuda_time_us": 435.48199999999997, + "pct_cuda_time": 0.659194567782163, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 434.714, + "pct_cuda_time": 0.6580320365453801, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2487.475, + "cuda_time_us": 2030.437, + "pct_cuda_time": 3.073497964609127, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.434, + "cuda_time_us": 32.799, + "pct_cuda_time": 0.04964825785838948, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.799, + "pct_cuda_time": 0.04964825785838948, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1788.459, + "cuda_time_us": 452.058, + "pct_cuda_time": 0.6842858669760612, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 158.112, + "cuda_time_us": 204.605, + "pct_cuda_time": 0.30971315586194026, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 203.837, + "pct_cuda_time": 0.30855062462515737, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 486.111, + "cuda_time_us": 39.999, + "pct_cuda_time": 0.060546988203229395, + "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": 39.999, + "pct_cuda_time": 0.060546988203229395, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 802.366, + "cuda_time_us": 62.528, + "pct_cuda_time": 0.09464941819474305, + "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": 16.0, + "pct_cuda_time": 0.024219400766310913, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.28, + "pct_cuda_time": 0.06854090416865988, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.0018891132597722512, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 194.415, + "cuda_time_us": 144.926, + "pct_cuda_time": 0.21937630471614844, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 144.158, + "pct_cuda_time": 0.21821377347936555, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.478, + "cuda_time_us": 31.295, + "pct_cuda_time": 0.04737163418635626, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.295, + "pct_cuda_time": 0.04737163418635626, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 478.142, + "cuda_time_us": 1514.2849999999999, + "pct_cuda_time": 2.2921922055883197, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.766, + "cuda_time_us": 948.788, + "pct_cuda_time": 1.4361923008916624, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 948.052, + "pct_cuda_time": 1.4350782084564122, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.525, + "cuda_time_us": 130.207, + "pct_cuda_time": 0.1970959697236903, + "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": 130.207, + "pct_cuda_time": 0.1970959697236903, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.328, + "cuda_time_us": 435.28999999999996, + "pct_cuda_time": 0.6589039349729673, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 434.522, + "pct_cuda_time": 0.6577414037361844, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2888.955, + "cuda_time_us": 2033.6390000000001, + "pct_cuda_time": 3.0783448721874853, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.29, + "cuda_time_us": 33.12, + "pct_cuda_time": 0.05013415958626359, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.12, + "pct_cuda_time": 0.05013415958626359, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1999.866, + "cuda_time_us": 454.65, + "pct_cuda_time": 0.6882094099002035, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.091, + "cuda_time_us": 205.951, + "pct_cuda_time": 0.31175061295140616, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011156061477981963, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 205.214, + "pct_cuda_time": 0.31063500680360795, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 472.58, + "cuda_time_us": 39.967, + "pct_cuda_time": 0.06049854940169677, + "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": 39.967, + "pct_cuda_time": 0.06049854940169677, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 786.207, + "cuda_time_us": 63.166999999999994, + "pct_cuda_time": 0.0956166805128476, + "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": 15.904, + "pct_cuda_time": 0.024074084361713047, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.631, + "pct_cuda_time": 0.06907221727297083, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.0024703788781637133, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 314.458, + "cuda_time_us": 145.565, + "pct_cuda_time": 0.22034356703425298, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 144.797, + "pct_cuda_time": 0.2191810357974701, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 116.351, + "cuda_time_us": 30.144, + "pct_cuda_time": 0.04562935104372976, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.144, + "pct_cuda_time": 0.04562935104372976, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 611.467, + "cuda_time_us": 1515.7250000000001, + "pct_cuda_time": 2.2943719516572885, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 222.296, + "cuda_time_us": 950.4200000000001, + "pct_cuda_time": 1.4386626797698263, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 949.652, + "pct_cuda_time": 1.4375001485330434, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 136.767, + "cuda_time_us": 129.822, + "pct_cuda_time": 0.19651319039275097, + "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": 129.822, + "pct_cuda_time": 0.19651319039275097, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 182.085, + "cuda_time_us": 435.483, + "pct_cuda_time": 0.659196081494711, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 434.747, + "pct_cuda_time": 0.6580819890594607, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 3115.353, + "cuda_time_us": 2032.518, + "pct_cuda_time": 3.0766480004212955, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.558, + "cuda_time_us": 33.183, + "pct_cuda_time": 0.05022952347678094, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.183, + "pct_cuda_time": 0.05022952347678094, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2363.093, + "cuda_time_us": 454.682, + "pct_cuda_time": 0.6882578487017362, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 164.645, + "cuda_time_us": 205.501, + "pct_cuda_time": 0.31106944230485367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.767, + "pct_cuda_time": 0.0011610175242350294, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 204.734, + "pct_cuda_time": 0.3099084247806187, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 581.425, + "cuda_time_us": 39.711, + "pct_cuda_time": 0.06011103898943579, + "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": 39.711, + "pct_cuda_time": 0.06011103898943579, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1167.075, + "cuda_time_us": 63.296, + "pct_cuda_time": 0.09581194943152598, + "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": 16.128, + "pct_cuda_time": 0.0244131559724414, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.696, + "pct_cuda_time": 0.06917060858858397, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.002228184870500604, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 266.011, + "cuda_time_us": 146.174, + "pct_cuda_time": 0.22126541797592073, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 145.406, + "pct_cuda_time": 0.2201028867391378, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 91.926, + "cuda_time_us": 30.272, + "pct_cuda_time": 0.045823106249860246, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.272, + "pct_cuda_time": 0.045823106249860246, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 491.974, + "cuda_time_us": 1514.381, + "pct_cuda_time": 2.292337521992918, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 171.915, + "cuda_time_us": 949.1080000000001, + "pct_cuda_time": 1.4366766889069889, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.769, + "pct_cuda_time": 0.0011640449493308183, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 948.339, + "pct_cuda_time": 1.435512643957658, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 106.544, + "cuda_time_us": 130.494, + "pct_cuda_time": 0.19753040522493603, + "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": 130.494, + "pct_cuda_time": 0.19753040522493603, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 153.65, + "cuda_time_us": 434.779, + "pct_cuda_time": 0.6581304278609933, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 434.043, + "pct_cuda_time": 0.657016335425743, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2741.425, + "cuda_time_us": 2036.71, + "pct_cuda_time": 3.0829934834220687, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.997, + "cuda_time_us": 33.471, + "pct_cuda_time": 0.05066547269057453, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.471, + "pct_cuda_time": 0.05066547269057453, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1955.968, + "cuda_time_us": 453.627, + "pct_cuda_time": 0.6866608819637076, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.249, + "cuda_time_us": 206.078, + "pct_cuda_time": 0.3119428544449888, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 205.31, + "pct_cuda_time": 0.31078032320820587, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 591.083, + "cuda_time_us": 39.839, + "pct_cuda_time": 0.06030479419556628, + "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": 39.839, + "pct_cuda_time": 0.06030479419556628, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 822.337, + "cuda_time_us": 62.592, + "pct_cuda_time": 0.0947462957978083, + "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": 15.648, + "pct_cuda_time": 0.023686573949452075, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.536, + "pct_cuda_time": 0.06892841458092086, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.00213130726743536, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 219.937, + "cuda_time_us": 145.118, + "pct_cuda_time": 0.21966693752534422, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 144.382, + "pct_cuda_time": 0.2185528450900939, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 88.178, + "cuda_time_us": 30.656, + "pct_cuda_time": 0.046404371868251706, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.656, + "pct_cuda_time": 0.046404371868251706, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 543.528, + "cuda_time_us": 1518.9560000000001, + "pct_cuda_time": 2.299262756899535, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 185.959, + "cuda_time_us": 953.7479999999999, + "pct_cuda_time": 1.4437003151292187, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 953.012, + "pct_cuda_time": 1.4425862226939685, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 124.146, + "cuda_time_us": 129.982, + "pct_cuda_time": 0.19675538440041407, + "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": 129.982, + "pct_cuda_time": 0.19675538440041407, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 166.698, + "cuda_time_us": 435.226, + "pct_cuda_time": 0.6588070573699021, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 434.458, + "pct_cuda_time": 0.6576445261331192, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2845.431, + "cuda_time_us": 2029.3829999999998, + "pct_cuda_time": 3.071902511583646, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.182, + "cuda_time_us": 33.088, + "pct_cuda_time": 0.050085720784730975, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.088, + "pct_cuda_time": 0.050085720784730975, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2108.896, + "cuda_time_us": 451.388, + "pct_cuda_time": 0.683271679568972, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 157.994, + "cuda_time_us": 204.285, + "pct_cuda_time": 0.30922876784661407, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 203.549, + "pct_cuda_time": 0.3081146754113638, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 638.429, + "cuda_time_us": 39.968, + "pct_cuda_time": 0.06050006311424466, + "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": 39.968, + "pct_cuda_time": 0.06050006311424466, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 900.291, + "cuda_time_us": 62.048, + "pct_cuda_time": 0.09392283617175373, + "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": 15.744, + "pct_cuda_time": 0.02383189035404994, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.024, + "pct_cuda_time": 0.06815339375639891, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.001937552061304873, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 242.671, + "cuda_time_us": 145.087, + "pct_cuda_time": 0.21962001243635948, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 144.351, + "pct_cuda_time": 0.21850592000110916, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.986, + "cuda_time_us": 30.656, + "pct_cuda_time": 0.046404371868251706, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.656, + "pct_cuda_time": 0.046404371868251706, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 496.446, + "cuda_time_us": 1514.251, + "pct_cuda_time": 2.2921407393616917, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.341, + "cuda_time_us": 949.299, + "pct_cuda_time": 1.4369658080036365, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0011125787227024076, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 948.564, + "pct_cuda_time": 1.435853229280934, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 108.908, + "cuda_time_us": 130.366, + "pct_cuda_time": 0.19733665001880557, + "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": 130.366, + "pct_cuda_time": 0.19733665001880557, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 157.194, + "cuda_time_us": 434.586, + "pct_cuda_time": 0.6578382813392497, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 433.85, + "pct_cuda_time": 0.6567241889039994, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2540.243, + "cuda_time_us": 2033.7669999999998, + "pct_cuda_time": 3.0785386273936153, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.825, + "cuda_time_us": 33.151, + "pct_cuda_time": 0.05018108467524832, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.151, + "pct_cuda_time": 0.05018108467524832, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1815.744, + "cuda_time_us": 453.755, + "pct_cuda_time": 0.686854637169838, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 159.711, + "cuda_time_us": 204.797, + "pct_cuda_time": 0.310003788671136, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 204.061, + "pct_cuda_time": 0.30888969623588575, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 530.705, + "cuda_time_us": 39.84, + "pct_cuda_time": 0.060306307908114185, + "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": 39.84, + "pct_cuda_time": 0.060306307908114185, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 762.213, + "cuda_time_us": 63.392, + "pct_cuda_time": 0.09595726583612385, + "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": 16.16, + "pct_cuda_time": 0.024461594773974027, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.76, + "pct_cuda_time": 0.06926748619164921, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.002228184870500604, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 202.64, + "cuda_time_us": 145.726, + "pct_cuda_time": 0.22058727475446402, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 144.958, + "pct_cuda_time": 0.21942474351768107, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.749, + "cuda_time_us": 30.687, + "pct_cuda_time": 0.04645129695723644, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.687, + "pct_cuda_time": 0.04645129695723644, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 484.029, + "cuda_time_us": 1516.174, + "pct_cuda_time": 2.295051608591293, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 161.763, + "cuda_time_us": 951.605, + "pct_cuda_time": 1.4404564291390811, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 950.837, + "pct_cuda_time": 1.4392938979022982, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.542, + "cuda_time_us": 129.535, + "pct_cuda_time": 0.19607875489150525, + "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": 129.535, + "pct_cuda_time": 0.19607875489150525, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 157.368, + "cuda_time_us": 435.034, + "pct_cuda_time": 0.6585164245607064, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0011125787227024076, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 434.299, + "pct_cuda_time": 0.6574038458380039, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2623.358, + "cuda_time_us": 2029.9589999999998, + "pct_cuda_time": 3.0727744100112333, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.461, + "cuda_time_us": 32.352, + "pct_cuda_time": 0.048971628349480656, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.352, + "pct_cuda_time": 0.048971628349480656, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1914.502, + "cuda_time_us": 454.009, + "pct_cuda_time": 0.6872391201570033, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 159.441, + "cuda_time_us": 206.49200000000002, + "pct_cuda_time": 0.3125695314398171, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0011125787227024076, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 205.757, + "pct_cuda_time": 0.31145695271711465, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 530.02, + "cuda_time_us": 39.904, + "pct_cuda_time": 0.06040318551117943, + "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": 39.904, + "pct_cuda_time": 0.06040318551117943, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 835.864, + "cuda_time_us": 62.974999999999994, + "pct_cuda_time": 0.09532604770365186, + "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": 15.968, + "pct_cuda_time": 0.024170961964778293, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.599, + "pct_cuda_time": 0.0690237784714382, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.00213130726743536, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 195.652, + "cuda_time_us": 144.638, + "pct_cuda_time": 0.21894035550235488, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 143.87, + "pct_cuda_time": 0.21777782426557196, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.687, + "cuda_time_us": 31.232, + "pct_cuda_time": 0.047276270295838904, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.232, + "pct_cuda_time": 0.047276270295838904, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 476.49, + "cuda_time_us": 1512.366, + "pct_cuda_time": 2.2892873912089104, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.048, + "cuda_time_us": 948.437, + "pct_cuda_time": 1.4356609877873514, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011156061477981963, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 947.7, + "pct_cuda_time": 1.4345453816395535, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.154, + "cuda_time_us": 130.078, + "pct_cuda_time": 0.19690070080501196, + "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": 130.078, + "pct_cuda_time": 0.19690070080501196, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 159.499, + "cuda_time_us": 433.851, + "pct_cuda_time": 0.6567257026165473, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 433.083, + "pct_cuda_time": 0.6555631713797644, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 3017.443, + "cuda_time_us": 2034.44, + "pct_cuda_time": 3.079557355938349, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.536, + "cuda_time_us": 33.344, + "pct_cuda_time": 0.050473231196991944, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.344, + "pct_cuda_time": 0.050473231196991944, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2157.319, + "cuda_time_us": 453.6909999999999, + "pct_cuda_time": 0.6867577595667727, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.362, + "cuda_time_us": 205.981, + "pct_cuda_time": 0.31179602432784304, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 205.245, + "pct_cuda_time": 0.3106819318925927, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 532.628, + "cuda_time_us": 39.808, + "pct_cuda_time": 0.060257869106581555, + "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": 39.808, + "pct_cuda_time": 0.060257869106581555, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 982.569, + "cuda_time_us": 62.400000000000006, + "pct_cuda_time": 0.09445566298861258, + "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": 15.648, + "pct_cuda_time": 0.023686573949452075, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.472, + "pct_cuda_time": 0.06883153697785561, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.001937552061304873, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 312.041, + "cuda_time_us": 145.50199999999998, + "pct_cuda_time": 0.2202482031437356, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 144.766, + "pct_cuda_time": 0.21913411070848535, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 102.994, + "cuda_time_us": 30.752, + "pct_cuda_time": 0.046549688272849575, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.752, + "pct_cuda_time": 0.046549688272849575, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 599.789, + "cuda_time_us": 1516.653, + "pct_cuda_time": 2.295776676901734, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 224.067, + "cuda_time_us": 950.484, + "pct_cuda_time": 1.4387595573728915, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 949.748, + "pct_cuda_time": 1.4376454649376413, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 128.901, + "cuda_time_us": 130.334, + "pct_cuda_time": 0.1972882112172729, + "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": 130.334, + "pct_cuda_time": 0.1972882112172729, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 179.535, + "cuda_time_us": 435.835, + "pct_cuda_time": 0.6597289083115697, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 435.067, + "pct_cuda_time": 0.6585663770747869, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2854.743, + "cuda_time_us": 2034.504, + "pct_cuda_time": 3.079654233541414, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.963, + "cuda_time_us": 32.799, + "pct_cuda_time": 0.04964825785838948, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.799, + "pct_cuda_time": 0.04964825785838948, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2035.391, + "cuda_time_us": 452.95500000000004, + "pct_cuda_time": 0.6856436671315225, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 169.76, + "cuda_time_us": 204.734, + "pct_cuda_time": 0.3099084247806187, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 203.966, + "pct_cuda_time": 0.30874589354383575, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 537.299, + "cuda_time_us": 39.968, + "pct_cuda_time": 0.06050006311424466, + "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": 39.968, + "pct_cuda_time": 0.06050006311424466, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 881.088, + "cuda_time_us": 62.528, + "pct_cuda_time": 0.09464941819474305, + "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": 15.68, + "pct_cuda_time": 0.023735012750984694, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.568, + "pct_cuda_time": 0.06897685338245348, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.001937552061304873, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 261.933, + "cuda_time_us": 145.725, + "pct_cuda_time": 0.22058576104191613, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 144.989, + "pct_cuda_time": 0.2194716686066658, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 95.133, + "cuda_time_us": 30.4, + "pct_cuda_time": 0.04601686145599074, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.4, + "pct_cuda_time": 0.04601686145599074, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 558.355, + "cuda_time_us": 1518.35, + "pct_cuda_time": 2.2983454470955107, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 221.679, + "cuda_time_us": 952.533, + "pct_cuda_time": 1.4418611543835271, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011156061477981963, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 951.796, + "pct_cuda_time": 1.440745548235729, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 110.612, + "cuda_time_us": 129.919, + "pct_cuda_time": 0.19666002050989675, + "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": 129.919, + "pct_cuda_time": 0.19666002050989675, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 167.142, + "cuda_time_us": 435.89799999999997, + "pct_cuda_time": 0.6598242722020871, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 435.162, + "pct_cuda_time": 0.6587101797668369, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 3016.391, + "cuda_time_us": 2031.46, + "pct_cuda_time": 3.075046492545623, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.92, + "cuda_time_us": 32.863, + "pct_cuda_time": 0.049745135461454724, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.863, + "pct_cuda_time": 0.049745135461454724, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2271.188, + "cuda_time_us": 452.731, + "pct_cuda_time": 0.6853045955207941, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 164.54, + "cuda_time_us": 204.702, + "pct_cuda_time": 0.30985998597908604, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 203.966, + "pct_cuda_time": 0.30874589354383575, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 559.956, + "cuda_time_us": 39.871, + "pct_cuda_time": 0.0603532329970989, + "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": 39.871, + "pct_cuda_time": 0.0603532329970989, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1128.709, + "cuda_time_us": 63.008, + "pct_cuda_time": 0.09537600021773239, + "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": 15.552, + "pct_cuda_time": 0.023541257544854206, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.792, + "pct_cuda_time": 0.06931592499318183, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.0025188176796963353, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 246.437, + "cuda_time_us": 145.14999999999998, + "pct_cuda_time": 0.2197153763268768, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 144.414, + "pct_cuda_time": 0.21860128389162653, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 91.53, + "cuda_time_us": 30.432, + "pct_cuda_time": 0.04606530025752336, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.432, + "pct_cuda_time": 0.04606530025752336, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 494.492, + "cuda_time_us": 1515.4340000000002, + "pct_cuda_time": 2.2939314613058515, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 170.978, + "cuda_time_us": 949.9390000000001, + "pct_cuda_time": 1.437934584034289, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 949.171, + "pct_cuda_time": 1.436772052797506, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 108.708, + "cuda_time_us": 129.79, + "pct_cuda_time": 0.19646475159121832, + "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": 129.79, + "pct_cuda_time": 0.19646475159121832, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 157.374, + "cuda_time_us": 435.70500000000004, + "pct_cuda_time": 0.6595321256803437, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0011125787227024076, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 434.97, + "pct_cuda_time": 0.6584195469576412, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2582.747, + "cuda_time_us": 2032.328, + "pct_cuda_time": 3.0763603950371956, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.942, + "cuda_time_us": 33.599, + "pct_cuda_time": 0.050859227896705016, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.599, + "pct_cuda_time": 0.050859227896705016, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1872.728, + "cuda_time_us": 454.363, + "pct_cuda_time": 0.6877749743989579, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.119, + "cuda_time_us": 205.822, + "pct_cuda_time": 0.31155534403272783, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.769, + "pct_cuda_time": 0.0011640449493308183, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 205.053, + "pct_cuda_time": 0.31039129908339697, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 557.875, + "cuda_time_us": 39.711, + "pct_cuda_time": 0.06011103898943579, + "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": 39.711, + "pct_cuda_time": 0.06011103898943579, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 780.603, + "cuda_time_us": 63.072, + "pct_cuda_time": 0.09547287782079764, + "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": 15.808, + "pct_cuda_time": 0.023928767957115182, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.792, + "pct_cuda_time": 0.06931592499318183, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.002228184870500604, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 217.458, + "cuda_time_us": 145.75799999999998, + "pct_cuda_time": 0.2206357135559966, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 145.022, + "pct_cuda_time": 0.21952162112074633, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.253, + "cuda_time_us": 30.495, + "pct_cuda_time": 0.04616066414804071, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.495, + "pct_cuda_time": 0.04616066414804071, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 478.267, + "cuda_time_us": 1513.871, + "pct_cuda_time": 2.291565528593492, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 162.488, + "cuda_time_us": 948.885, + "pct_cuda_time": 1.436339131008808, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 948.149, + "pct_cuda_time": 1.4352250385735579, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 105.122, + "cuda_time_us": 129.983, + "pct_cuda_time": 0.19675689811296196, + "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": 129.983, + "pct_cuda_time": 0.19675689811296196, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 156.406, + "cuda_time_us": 435.00300000000004, + "pct_cuda_time": 0.6584694994717217, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011156061477981963, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 434.266, + "pct_cuda_time": 0.6573538933239235, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2880.322, + "cuda_time_us": 2056.614, + "pct_cuda_time": 3.11312241797536, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.007, + "cuda_time_us": 32.96, + "pct_cuda_time": 0.04989196557860048, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.96, + "pct_cuda_time": 0.04989196557860048, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2025.203, + "cuda_time_us": 459.036, + "pct_cuda_time": 0.6948485531352686, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 246.737, + "cuda_time_us": 205.471, + "pct_cuda_time": 0.3110240309284169, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011156061477981963, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 204.734, + "pct_cuda_time": 0.3099084247806187, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 528.47, + "cuda_time_us": 39.391, + "pct_cuda_time": 0.059626650974109574, + "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": 39.391, + "pct_cuda_time": 0.059626650974109574, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 810.602, + "cuda_time_us": 63.008, + "pct_cuda_time": 0.09537600021773239, + "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": 16.0, + "pct_cuda_time": 0.024219400766310913, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 45.728, + "pct_cuda_time": 0.06921904739011658, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.001937552061304873, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 267.444, + "cuda_time_us": 151.166, + "pct_cuda_time": 0.2288218710150097, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 150.43, + "pct_cuda_time": 0.22770777857975943, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 96.786, + "cuda_time_us": 30.463, + "pct_cuda_time": 0.046112225346508086, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.463, + "pct_cuda_time": 0.046112225346508086, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 590.256, + "cuda_time_us": 1534.155, + "pct_cuda_time": 2.3222696739149824, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 178.34, + "cuda_time_us": 960.307, + "pct_cuda_time": 1.4536287557308585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0011125787227024076, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 959.572, + "pct_cuda_time": 1.452516177008156, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 107.525, + "cuda_time_us": 131.838, + "pct_cuda_time": 0.19956483488930613, + "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": 131.838, + "pct_cuda_time": 0.19956483488930613, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 243.553, + "cuda_time_us": 442.01, + "pct_cuda_time": 0.6690760832948179, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 441.274, + "pct_cuda_time": 0.6679619908595676, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2665.773, + "cuda_time_us": 2070.31, + "pct_cuda_time": 3.133854225031322, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 103.819, + "cuda_time_us": 33.088, + "pct_cuda_time": 0.050085720784730975, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.088, + "pct_cuda_time": 0.050085720784730975, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1903.847, + "cuda_time_us": 471.578, + "pct_cuda_time": 0.7138335359109604, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 175.231, + "cuda_time_us": 211.70999999999998, + "pct_cuda_time": 0.3204680835147302, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 210.974, + "pct_cuda_time": 0.31935399107947987, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 572.745, + "cuda_time_us": 40.8, + "pct_cuda_time": 0.06175947195409282, + "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": 40.8, + "pct_cuda_time": 0.06175947195409282, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 787.651, + "cuda_time_us": 65.598, + "pct_cuda_time": 0.09929651571677896, + "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": 16.287, + "pct_cuda_time": 0.024653836267556614, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 47.999, + "pct_cuda_time": 0.07265668858638485, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.001985990862837495, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 205.042, + "cuda_time_us": 153.47, + "pct_cuda_time": 0.2323094647253585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 152.702, + "pct_cuda_time": 0.2311469334885756, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.041, + "cuda_time_us": 31.2, + "pct_cuda_time": 0.04722783149430628, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.2, + "pct_cuda_time": 0.04722783149430628, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 477.532, + "cuda_time_us": 1534.444, + "pct_cuda_time": 2.322707136841324, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.475, + "cuda_time_us": 959.7959999999999, + "pct_cuda_time": 1.4528552486188844, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 959.06, + "pct_cuda_time": 1.451741156183634, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.286, + "cuda_time_us": 132.446, + "pct_cuda_time": 0.20048517211842595, + "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": 132.446, + "pct_cuda_time": 0.20048517211842595, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.174, + "cuda_time_us": 442.202, + "pct_cuda_time": 0.6693667161040137, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 441.466, + "pct_cuda_time": 0.6682526236687634, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2501.873, + "cuda_time_us": 2073.928, + "pct_cuda_time": 3.139330837029604, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.676, + "cuda_time_us": 32.96, + "pct_cuda_time": 0.04989196557860048, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.96, + "pct_cuda_time": 0.04989196557860048, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1798.647, + "cuda_time_us": 470.811, + "pct_cuda_time": 0.7126725183867254, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 158.588, + "cuda_time_us": 210.909, + "pct_cuda_time": 0.31925559976386675, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 210.173, + "pct_cuda_time": 0.31814150732861646, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 529.722, + "cuda_time_us": 40.672, + "pct_cuda_time": 0.06156571674796234, + "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": 40.672, + "pct_cuda_time": 0.06156571674796234, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 764.891, + "cuda_time_us": 65.598, + "pct_cuda_time": 0.09929651571677896, + "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": 15.999, + "pct_cuda_time": 0.02421788705376302, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 48.095, + "pct_cuda_time": 0.07280200499098272, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.0022766236720332257, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 186.911, + "cuda_time_us": 153.632, + "pct_cuda_time": 0.2325546861581174, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011156061477981963, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 152.895, + "pct_cuda_time": 0.2314390800103192, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.111, + "cuda_time_us": 31.616, + "pct_cuda_time": 0.047857535914230365, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.616, + "pct_cuda_time": 0.047857535914230365, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 469.103, + "cuda_time_us": 1538.541, + "pct_cuda_time": 2.3289088171500474, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.952, + "cuda_time_us": 962.74, + "pct_cuda_time": 1.4573116183598855, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 961.972, + "pct_cuda_time": 1.4561490871231026, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.271, + "cuda_time_us": 132.254, + "pct_cuda_time": 0.2001945393092302, + "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": 132.254, + "pct_cuda_time": 0.2001945393092302, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.282, + "cuda_time_us": 443.54699999999997, + "pct_cuda_time": 0.6714026594809316, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 442.811, + "pct_cuda_time": 0.6702885670456814, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2519.858, + "cuda_time_us": 2068.454, + "pct_cuda_time": 3.1310447745424295, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.775, + "cuda_time_us": 32.992, + "pct_cuda_time": 0.0499404043801331, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.992, + "pct_cuda_time": 0.0499404043801331, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1821.963, + "cuda_time_us": 471.003, + "pct_cuda_time": 0.7129631511959212, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 157.964, + "cuda_time_us": 211.13299999999998, + "pct_cuda_time": 0.31959467137459513, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 210.397, + "pct_cuda_time": 0.31848057893934484, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 527.955, + "cuda_time_us": 40.96, + "pct_cuda_time": 0.062001665961755936, + "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": 40.96, + "pct_cuda_time": 0.062001665961755936, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 782.058, + "cuda_time_us": 65.343, + "pct_cuda_time": 0.09891051901706588, + "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": 16.031, + "pct_cuda_time": 0.024266325855295638, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 47.84, + "pct_cuda_time": 0.07241600829126964, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.002228184870500604, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 196.839, + "cuda_time_us": 153.567, + "pct_cuda_time": 0.23245629484250427, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 152.799, + "pct_cuda_time": 0.23129376360572135, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.578, + "cuda_time_us": 31.839, + "pct_cuda_time": 0.04819509381241082, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.839, + "pct_cuda_time": 0.04819509381241082, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 473.453, + "cuda_time_us": 1532.6200000000001, + "pct_cuda_time": 2.319946125153965, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.564, + "cuda_time_us": 959.412, + "pct_cuda_time": 1.452273983000493, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 958.676, + "pct_cuda_time": 1.4511598905652427, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 108.259, + "cuda_time_us": 132.062, + "pct_cuda_time": 0.1999039065000345, + "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": 132.062, + "pct_cuda_time": 0.1999039065000345, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.916, + "cuda_time_us": 441.146, + "pct_cuda_time": 0.6677682356534371, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.767, + "pct_cuda_time": 0.0011610175242350294, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 440.379, + "pct_cuda_time": 0.6666072181292022, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2911.906, + "cuda_time_us": 2064.196, + "pct_cuda_time": 3.124599386513495, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.318, + "cuda_time_us": 32.768, + "pct_cuda_time": 0.04960133276940476, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.768, + "pct_cuda_time": 0.04960133276940476, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2103.341, + "cuda_time_us": 467.928, + "pct_cuda_time": 0.7083084851111459, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.954, + "cuda_time_us": 210.78099999999998, + "pct_cuda_time": 0.31906184455773623, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 210.045, + "pct_cuda_time": 0.317947752122486, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 504.35, + "cuda_time_us": 40.448, + "pct_cuda_time": 0.061226645137233984, + "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": 40.448, + "pct_cuda_time": 0.061226645137233984, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 981.451, + "cuda_time_us": 65.022, + "pct_cuda_time": 0.09842461728919179, + "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": 15.872, + "pct_cuda_time": 0.024025645560180428, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 47.807, + "pct_cuda_time": 0.07236605577718912, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 1.343, + "pct_cuda_time": 0.0020329159518222223, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 305.272, + "cuda_time_us": 151.67700000000002, + "pct_cuda_time": 0.22959537812698383, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0011125787227024076, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 150.942, + "pct_cuda_time": 0.2284827994042814, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 107.416, + "cuda_time_us": 30.463, + "pct_cuda_time": 0.046112225346508086, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.463, + "pct_cuda_time": 0.046112225346508086, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 550.91, + "cuda_time_us": 1533.0369999999998, + "pct_cuda_time": 2.3205773432864363, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 183.617, + "cuda_time_us": 958.26, + "pct_cuda_time": 1.4505301861453184, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 957.524, + "pct_cuda_time": 1.4494160937100682, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 116.124, + "cuda_time_us": 132.415, + "pct_cuda_time": 0.2004382470294412, + "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": 132.415, + "pct_cuda_time": 0.2004382470294412, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 189.627, + "cuda_time_us": 442.36199999999997, + "pct_cuda_time": 0.6696089101116768, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.984, + "pct_cuda_time": 0.0030032056950225535, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 440.378, + "pct_cuda_time": 0.6666057044166542, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2966.785, + "cuda_time_us": 2066.468, + "pct_cuda_time": 3.1280385414223106, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.467, + "cuda_time_us": 33.344, + "pct_cuda_time": 0.050473231196991944, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.344, + "pct_cuda_time": 0.050473231196991944, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2240.441, + "cuda_time_us": 468.89, + "pct_cuda_time": 0.7097646765822202, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 162.581, + "cuda_time_us": 210.78099999999998, + "pct_cuda_time": 0.31906184455773623, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 210.045, + "pct_cuda_time": 0.317947752122486, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 586.85, + "cuda_time_us": 40.959, + "pct_cuda_time": 0.06200015224920805, + "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": 40.959, + "pct_cuda_time": 0.06200015224920805, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1054.79, + "cuda_time_us": 64.479, + "pct_cuda_time": 0.09760267137568508, + "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": 16.128, + "pct_cuda_time": 0.0244131559724414, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 47.071, + "pct_cuda_time": 0.07125196334193881, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.001937552061304873, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 254.706, + "cuda_time_us": 152.671, + "pct_cuda_time": 0.23110000839959086, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 151.903, + "pct_cuda_time": 0.2299374771628079, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.867, + "cuda_time_us": 31.263, + "pct_cuda_time": 0.04732319538482364, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.263, + "pct_cuda_time": 0.04732319538482364, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 482.999, + "cuda_time_us": 1532.971, + "pct_cuda_time": 2.3204774382582753, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.297, + "cuda_time_us": 960.018, + "pct_cuda_time": 1.453191292804517, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.767, + "pct_cuda_time": 0.0011610175242350294, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 959.251, + "pct_cuda_time": 1.4520302752802818, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 107.223, + "cuda_time_us": 131.807, + "pct_cuda_time": 0.19951790980032139, + "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": 131.807, + "pct_cuda_time": 0.19951790980032139, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 157.663, + "cuda_time_us": 441.146, + "pct_cuda_time": 0.6677682356534371, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 440.41, + "pct_cuda_time": 0.666654143218187, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2644.506, + "cuda_time_us": 2066.6319999999996, + "pct_cuda_time": 3.128286790280165, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.314, + "cuda_time_us": 32.544, + "pct_cuda_time": 0.04926226115867639, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.544, + "pct_cuda_time": 0.04926226115867639, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1909.555, + "cuda_time_us": 469.08399999999995, + "pct_cuda_time": 0.7100583368165116, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 153.423, + "cuda_time_us": 210.71699999999998, + "pct_cuda_time": 0.318964966954671, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 209.981, + "pct_cuda_time": 0.31785087451942073, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 590.938, + "cuda_time_us": 41.024, + "pct_cuda_time": 0.06209854356482118, + "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": 41.024, + "pct_cuda_time": 0.06209854356482118, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 784.412, + "cuda_time_us": 65.536, + "pct_cuda_time": 0.09920266553880952, + "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": 16.096, + "pct_cuda_time": 0.02436471717090878, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 47.935, + "pct_cuda_time": 0.07255981098331961, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.0022781373845811204, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 226.009, + "cuda_time_us": 151.807, + "pct_cuda_time": 0.22979216075821007, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011156061477981963, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 151.07, + "pct_cuda_time": 0.22867655461041186, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 93.518, + "cuda_time_us": 31.232, + "pct_cuda_time": 0.047276270295838904, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.232, + "pct_cuda_time": 0.047276270295838904, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 490.26, + "cuda_time_us": 1533.772, + "pct_cuda_time": 2.321689922009139, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 173.381, + "cuda_time_us": 960.436, + "pct_cuda_time": 1.4538240246495369, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 959.7, + "pct_cuda_time": 1.4527099322142867, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.363, + "cuda_time_us": 131.774, + "pct_cuda_time": 0.1994679572862409, + "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": 131.774, + "pct_cuda_time": 0.1994679572862409, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.574, + "cuda_time_us": 441.562, + "pct_cuda_time": 0.6683979400733613, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0011125787227024076, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 440.827, + "pct_cuda_time": 0.6672853613506587, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2563.664, + "cuda_time_us": 2067.078, + "pct_cuda_time": 3.128961906076527, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.867, + "cuda_time_us": 32.831, + "pct_cuda_time": 0.04969669665992211, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.831, + "pct_cuda_time": 0.04969669665992211, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1846.497, + "cuda_time_us": 470.77799999999996, + "pct_cuda_time": 0.7126225658726449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 154.586, + "cuda_time_us": 211.22899999999998, + "pct_cuda_time": 0.319739987779193, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 210.493, + "pct_cuda_time": 0.3186258953439427, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 568.004, + "cuda_time_us": 41.376, + "pct_cuda_time": 0.06263137038168001, + "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": 41.376, + "pct_cuda_time": 0.06263137038168001, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 767.729, + "cuda_time_us": 66.07900000000001, + "pct_cuda_time": 0.1000246114523162, + "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": 16.16, + "pct_cuda_time": 0.024461594773974027, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 48.383, + "pct_cuda_time": 0.07323795420477631, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.0023250624735658477, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 199.431, + "cuda_time_us": 152.094, + "pct_cuda_time": 0.23022659625945574, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 151.358, + "pct_cuda_time": 0.22911250382420548, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.61, + "cuda_time_us": 31.04, + "pct_cuda_time": 0.046985637486643174, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.04, + "pct_cuda_time": 0.046985637486643174, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 489.799, + "cuda_time_us": 1532.429, + "pct_cuda_time": 2.3196570060573167, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 180.679, + "cuda_time_us": 958.356, + "pct_cuda_time": 1.4506755025499163, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 957.62, + "pct_cuda_time": 1.449561410114666, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.863, + "cuda_time_us": 132.095, + "pct_cuda_time": 0.19995385901411503, + "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": 132.095, + "pct_cuda_time": 0.19995385901411503, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 153.869, + "cuda_time_us": 441.978, + "pct_cuda_time": 0.6690276444932853, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 441.242, + "pct_cuda_time": 0.667913552058035, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2507.642, + "cuda_time_us": 2067.589, + "pct_cuda_time": 3.129735413188501, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.566, + "cuda_time_us": 32.895, + "pct_cuda_time": 0.049793574262987354, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.895, + "pct_cuda_time": 0.049793574262987354, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1830.203, + "cuda_time_us": 472.506, + "pct_cuda_time": 0.7152382611554065, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.781, + "cuda_time_us": 212.862, + "pct_cuda_time": 0.3222118803699046, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 212.094, + "pct_cuda_time": 0.32104934913312166, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 509.875, + "cuda_time_us": 41.023, + "pct_cuda_time": 0.06209702985227329, + "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": 41.023, + "pct_cuda_time": 0.06209702985227329, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 822.505, + "cuda_time_us": 65.726, + "pct_cuda_time": 0.09949027092290945, + "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": 16.16, + "pct_cuda_time": 0.024461594773974027, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 48.063, + "pct_cuda_time": 0.0727535661894501, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.0022751099594853314, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 200.903, + "cuda_time_us": 152.89499999999998, + "pct_cuda_time": 0.23143908001031915, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 152.159, + "pct_cuda_time": 0.2303249875750689, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.025, + "cuda_time_us": 30.688, + "pct_cuda_time": 0.04645281066978433, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.688, + "pct_cuda_time": 0.04645281066978433, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 459.063, + "cuda_time_us": 1531.5, + "pct_cuda_time": 2.3182507671003227, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.985, + "cuda_time_us": 958.932, + "pct_cuda_time": 1.4515474009775036, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 958.196, + "pct_cuda_time": 1.4504333085422534, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.248, + "cuda_time_us": 132.094, + "pct_cuda_time": 0.1999523453015671, + "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": 132.094, + "pct_cuda_time": 0.1999523453015671, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 149.351, + "cuda_time_us": 440.474, + "pct_cuda_time": 0.666751020821252, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 439.738, + "pct_cuda_time": 0.6656369283860017, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2569.713, + "cuda_time_us": 2067.523, + "pct_cuda_time": 3.1296355081603404, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.008, + "cuda_time_us": 32.639, + "pct_cuda_time": 0.04940606385072637, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.639, + "pct_cuda_time": 0.04940606385072637, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1848.2, + "cuda_time_us": 469.27299999999997, + "pct_cuda_time": 0.7103444284880638, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.412, + "cuda_time_us": 211.22899999999998, + "pct_cuda_time": 0.319739987779193, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 210.493, + "pct_cuda_time": 0.3186258953439427, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 538.932, + "cuda_time_us": 40.735, + "pct_cuda_time": 0.06166108063847969, + "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": 40.735, + "pct_cuda_time": 0.06166108063847969, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 772.459, + "cuda_time_us": 65.503, + "pct_cuda_time": 0.09915271302472899, + "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": 16.224, + "pct_cuda_time": 0.024558472377039266, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 47.775, + "pct_cuda_time": 0.07231761697565649, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.0022766236720332257, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 232.844, + "cuda_time_us": 151.80599999999998, + "pct_cuda_time": 0.22979064704566213, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 151.07, + "pct_cuda_time": 0.22867655461041186, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 88.785, + "cuda_time_us": 31.488, + "pct_cuda_time": 0.04766378070809988, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.488, + "pct_cuda_time": 0.04766378070809988, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 491.868, + "cuda_time_us": 1534.123, + "pct_cuda_time": 2.32222123511345, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.435, + "cuda_time_us": 960.979, + "pct_cuda_time": 1.4546459705630435, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 960.243, + "pct_cuda_time": 1.4535318781277933, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.251, + "cuda_time_us": 131.774, + "pct_cuda_time": 0.1994679572862409, + "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": 131.774, + "pct_cuda_time": 0.1994679572862409, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 157.847, + "cuda_time_us": 441.37, + "pct_cuda_time": 0.6681073072641655, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 440.634, + "pct_cuda_time": 0.6669932148289153, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2439.791, + "cuda_time_us": 2063.3959999999997, + "pct_cuda_time": 3.123388416475179, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.164, + "cuda_time_us": 32.544, + "pct_cuda_time": 0.04926226115867639, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.544, + "pct_cuda_time": 0.04926226115867639, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1705.392, + "cuda_time_us": 468.18399999999997, + "pct_cuda_time": 0.7086959955234068, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.883, + "cuda_time_us": 211.261, + "pct_cuda_time": 0.3197884265807256, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011625312367829238, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 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": 210.493, + "pct_cuda_time": 0.3186258953439427, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3072, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 496.111, + "cuda_time_us": 40.223, + "pct_cuda_time": 0.06088605981395774, + "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": 40.223, + "pct_cuda_time": 0.06088605981395774, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 723.49, + "cuda_time_us": 65.279, + "pct_cuda_time": 0.09881364141400063, + "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": 16.064, + "pct_cuda_time": 0.02431627836937616, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3072], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 47.679, + "pct_cuda_time": 0.07217230057105864, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 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": 0.0023250624735658477, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[13], int32[13], 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[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], bfloat16[3072, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 191.982, + "cuda_time_us": 151.42100000000002, + "pct_cuda_time": 0.22920786771472285, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0011125787227024076, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 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": 150.686, + "pct_cuda_time": 0.22809528899202042, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3072, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 103.773, + "cuda_time_us": 30.592, + "pct_cuda_time": 0.04630749426518647, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.592, + "pct_cuda_time": 0.04630749426518647, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 484.382, + "cuda_time_us": 1532.076, + "pct_cuda_time": 2.3191226655279102, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.038, + "cuda_time_us": 957.78, + "pct_cuda_time": 1.449803604122329, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 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": 957.044, + "pct_cuda_time": 1.4486895116870788, + "trace": "mm(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3072, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3072, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.037, + "cuda_time_us": 132.19, + "pct_cuda_time": 0.200097661706165, + "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": 132.19, + "pct_cuda_time": 0.200097661706165, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 173.366, + "cuda_time_us": 442.106, + "pct_cuda_time": 0.6692213996994159, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 441.37, + "pct_cuda_time": 0.6681073072641655, + "trace": "mm(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3072, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3072, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.682, + "cuda_time_us": 33.248, + "pct_cuda_time": 0.05032791479239407, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.248, + "pct_cuda_time": 0.05032791479239407, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 447.93, + "cuda_time_us": 370.875, + "pct_cuda_time": 0.5613981412003475, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 8.768, + "pct_cuda_time": 0.01327223161993838, + "trace": "index_select(bfloat16[3072, 4096], 0, int64[12])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001114092435250302, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[12, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 361.371, + "pct_cuda_time": 0.5470118171451588, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[12, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 3944.254, + "cuda_time_us": 130.782, + "pct_cuda_time": 0.19796635443872965, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.004698563748664317, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 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": 0.001114092435250302, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 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": 0.0011625312367829238, + "trace": "copy_(int32[12], int32[12], True) <- _to_copy(int32[12], 3, 0, None, None, True, None) <- to(int32[12], 3, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.767, + "pct_cuda_time": 0.0011610175242350294, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 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": 0.0011625312367829238, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0012109700383155456, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0012109700383155456, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 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": 6.848, + "pct_cuda_time": 0.010365903527981071, + "trace": "copy_(float32[12, 128256], bfloat16[12, 128256], False) <- _to_copy(bfloat16[12, 128256], 6, None, None, None, False, None) <- to(bfloat16[12, 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": 8.64, + "pct_cuda_time": 0.013078476413807893, + "trace": "div_(float32[12, 128256], bfloat16[12, 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": 35.839, + "pct_cuda_time": 0.05424994400398855, + "trace": "_softmax(float32[12, 128256], -1, False) <- softmax(float32[12, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::LogSoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 28.672, + "pct_cuda_time": 0.04340116617322916, + "trace": "_log_softmax(float32[12, 128256], -1, False) <- log_softmax(float32[12, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 1.824, + "pct_cuda_time": 0.0027610116873594444, + "trace": "copy_(int64[12], int32[12], False) <- _to_copy(int32[12], 4, None, None, None, False, None) <- to(int32[12], 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": 9.441, + "pct_cuda_time": 0.014290960164671334, + "trace": "index(float32[12, 128256], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, long, 4> >(at::native::ReduceOp, unsigned int, long, 4>)", + "cpu_time_us": 0, + "cuda_time_us": 28.543, + "pct_cuda_time": 0.043205897254550776, + "trace": "argmax(float32[12, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.0048923189547948045, + "trace": "copy_(int64[12], int64[12], False) <- _to_copy(int64[12], 4, 0, None, None, False, None) <- to(int64[12], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + }, + "decode_1": { + "metadata": { + "num_running_seqs": 12 + }, + "summary_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cuda_time_us": 6465.097999999999, + "pct_cuda_time": 93.13076989673085, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cuda_time_us": 9.568, + "pct_cuda_time": 0.13782856909081978, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cuda_time_us": 9.568, + "pct_cuda_time": 0.13782856909081978, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cuda_time_us": 6452.201999999999, + "pct_cuda_time": 92.94500095578235, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cuda_time_us": 199.93600000000006, + "pct_cuda_time": 2.880110032372717, + "invocations": 64 + }, + "children": [ + { + "entry": { + "name": "void vllm::rms_norm_kernel(c10::BFloat16*, c10::BFloat16 const*, c10::BFloat16 const*, float, int, int)", + "cuda_time_us": 4.416, + "pct_cuda_time": 0.06361318573422452, + "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": 195.52000000000007, + "pct_cuda_time": 2.816496846638492, + "invocations": 63 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cuda_time_us": 1880.419, + "pct_cuda_time": 27.087736210408682, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cuda_time_us": 686.1640000000001, + "pct_cuda_time": 9.8843020779299, + "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": 686.1640000000001, + "pct_cuda_time": 9.8843020779299, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cuda_time_us": 119.38700000000001, + "pct_cuda_time": 1.7197888145950777, + "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": 119.38700000000001, + "pct_cuda_time": 1.7197888145950777, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cuda_time_us": 562.7450000000001, + "pct_cuda_time": 8.106431658968791, + "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": 78.71800000000002, + "pct_cuda_time": 1.1339453701600288, + "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": 438.9069999999999, + "pct_cuda_time": 6.322525478046032, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cuda_time_us": 45.12, + "pct_cuda_time": 0.6499608107627288, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cuda_time_us": 512.123, + "pct_cuda_time": 7.377213658914915, + "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": 512.123, + "pct_cuda_time": 7.377213658914915, + "invocations": 32 + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cuda_time_us": 4371.847000000001, + "pct_cuda_time": 62.97715471300097, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cuda_time_us": 2675.3250000000003, + "pct_cuda_time": 38.53848417672424, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cuda_time_us": 2675.3250000000003, + "pct_cuda_time": 38.53848417672424, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cuda_time_us": 288.191, + "pct_cuda_time": 4.15143741166936, + 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"std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cuda_time_us": 3.328, + "pct_cuda_time": 0.047940371857676446, + "invocations": 1 + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cuda_time_us": 351.42, + "pct_cuda_time": 5.062261261485775, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cuda_time_us": 7.168, + "pct_cuda_time": 0.10325618553961081, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cuda_time_us": 0.736, + "pct_cuda_time": 0.010602197622370753, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cuda_time_us": 343.516, + "pct_cuda_time": 4.948402878323793, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cuda_time_us": 125.439, + "pct_cuda_time": 1.806968841783376, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cuda_time_us": 5.3759999999999994, + "pct_cuda_time": 0.0774421391547081, + "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": 6.784, + "pct_cuda_time": 0.09772460417141737, + "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": 8.576, + "pct_cuda_time": 0.12353865055632009, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::SoftMaxForwardEpilogue>(float*, float const*, int)", + "cuda_time_us": 34.88, + "pct_cuda_time": 0.5024519742775705, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::LogSoftMaxForwardEpilogue>(float*, float const*, int)", + "cuda_time_us": 28.256, + "pct_cuda_time": 0.40703219567623367, + "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": 1.632, + "pct_cuda_time": 0.023509220814822103, + "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": 9.344, + "pct_cuda_time": 0.13460181329270693, + "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": 27.999, + "pct_cuda_time": 0.4033300696042917, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cuda_time_us": 2.592, + "pct_cuda_time": 0.03733817423530569, + "invocations": 1 + }, + "children": [] + } + ] + } + ], + "model_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cpu_time_us": 79697.371, + "cuda_time_us": 6465.097999999999, + "pct_cuda_time": 93.13076989673085, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cpu_time_us": 333.826, + "cuda_time_us": 9.568, + "pct_cuda_time": 0.13782856909081978, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 9.568, + "pct_cuda_time": 0.13782856909081978, + "trace": "index_select(bfloat16[128256, 4096], 0, int64[12]) <- embedding(bfloat16[128256, 4096], int64[12], -1, False, False)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 4474.612, + "cuda_time_us": 209.981, + "pct_cuda_time": 3.0248098626943385, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 327.087, + "cuda_time_us": 4.416, + "pct_cuda_time": 0.06361318573422452, + "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": 4.416, + "pct_cuda_time": 0.06361318573422452, + "trace": "_C::rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 3205.145, + "cuda_time_us": 65.855, + "pct_cuda_time": 0.9486517994853614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 477.52, + "cuda_time_us": 26.559, + "pct_cuda_time": 0.38258663947356636, + "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": 26.559, + "pct_cuda_time": 0.38258663947356636, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 1005.129, + "cuda_time_us": 3.872, + "pct_cuda_time": 0.055776778795950485, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.872, + "pct_cuda_time": 0.055776778795950485, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1150.502, + "cuda_time_us": 18.880000000000003, + "pct_cuda_time": 0.2719694172695106, + "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": 0.03457238355120898, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 14.976, + "pct_cuda_time": 0.21573167335954402, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.021665360358757626, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 278.634, + "cuda_time_us": 16.544, + "pct_cuda_time": 0.2383189639463339, + "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": 0.2383189639463339, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 153.448, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04425265094554749, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04425265094554749, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 645.477, + "cuda_time_us": 136.638, + "pct_cuda_time": 1.968292226529205, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 219.054, + "cuda_time_us": 83.807, + "pct_cuda_time": 1.2072532284484043, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.807, + "pct_cuda_time": 1.2072532284484043, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 176.587, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.13045312726656186, + "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": 0.13045312726656186, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 158.821, + "cuda_time_us": 43.775, + "pct_cuda_time": 0.6305858708142387, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.775, + "pct_cuda_time": 0.6305858708142387, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2504.975, + "cuda_time_us": 201.43900000000002, + "pct_cuda_time": 2.901760987571661, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.012, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04655747651562809, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04655747651562809, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1804.943, + "cuda_time_us": 59.135999999999996, + "pct_cuda_time": 0.8518635307017892, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.387, + "cuda_time_us": 21.92, + "pct_cuda_time": 0.315761103101042, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.92, + "pct_cuda_time": 0.315761103101042, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 561.336, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.05439388345390212, + "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": 0.05439388345390212, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 757.346, + "cuda_time_us": 17.28, + "pct_cuda_time": 0.24892116156870464, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.368, + "pct_cuda_time": 0.034111418437192856, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.632, + "pct_cuda_time": 0.19637113857086697, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01843860456064479, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 182.713, + "cuda_time_us": 16.16, + "pct_cuda_time": 0.23278738257814044, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.16, + "pct_cuda_time": 0.23278738257814044, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.892, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.043791685831531375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.043791685831531375, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 450.059, + "cuda_time_us": 136.031, + "pct_cuda_time": 1.959548294522712, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 153.97, + "cuda_time_us": 82.943, + "pct_cuda_time": 1.1948071703699692, + "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": 1.1948071703699692, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.599, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.12860926681049742, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.12860926681049742, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 137.831, + "cuda_time_us": 44.16, + "pct_cuda_time": 0.6361318573422451, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.16, + "pct_cuda_time": 0.6361318573422451, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2336.808, + "cuda_time_us": 201.663, + "pct_cuda_time": 2.9049877433697735, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.177, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.044728021219376614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044728021219376614, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1673.431, + "cuda_time_us": 57.92, + "pct_cuda_time": 0.8343468563691767, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 138.284, + "cuda_time_us": 20.736, + "pct_cuda_time": 0.29870539388244555, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.736, + "pct_cuda_time": 0.29870539388244555, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 492.471, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05531581368193436, + "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": 0.05531581368193436, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 719.54, + "cuda_time_us": 17.44, + "pct_cuda_time": 0.25122598713878525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.03549431377924122, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.696, + "pct_cuda_time": 0.1972930687988992, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01843860456064479, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 173.117, + "cuda_time_us": 15.904, + "pct_cuda_time": 0.22909966166601148, + "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": 15.904, + "pct_cuda_time": 0.22909966166601148, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.574, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04469921089975061, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04469921089975061, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 427.234, + "cuda_time_us": 137.535, + "pct_cuda_time": 1.9812136548814692, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 139.06, + "cuda_time_us": 83.423, + "pct_cuda_time": 1.2017216470802112, + "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": 83.423, + "pct_cuda_time": 1.2017216470802112, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.361, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.13045312726656186, + "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": 0.13045312726656186, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 135.231, + "cuda_time_us": 45.056, + "pct_cuda_time": 0.6490388805346965, + "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": 45.056, + "pct_cuda_time": 0.6490388805346965, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2350.899, + "cuda_time_us": 200.446, + "pct_cuda_time": 2.887456663877348, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.292, + "cuda_time_us": 3.264, + "pct_cuda_time": 0.047018441629644206, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.264, + "pct_cuda_time": 0.047018441629644206, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1647.634, + "cuda_time_us": 57.568, + "pct_cuda_time": 0.8292762401149993, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 134.746, + "cuda_time_us": 20.672, + "pct_cuda_time": 0.2977834636544133, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.672, + "pct_cuda_time": 0.2977834636544133, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 476.123, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05208905788382153, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05208905788382153, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 705.845, + "cuda_time_us": 17.696, + "pct_cuda_time": 0.25491370805091423, + "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": 0.035033348665225096, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.792, + "pct_cuda_time": 0.19867596414094757, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.021204395244741506, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 190.141, + "cuda_time_us": 15.584, + "pct_cuda_time": 0.2244900105258503, + "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": 15.584, + "pct_cuda_time": 0.2244900105258503, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 105.762, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04333072071751525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04333072071751525, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 455.343, + "cuda_time_us": 136.606, + "pct_cuda_time": 1.967831261415189, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 150.301, + "cuda_time_us": 83.903, + "pct_cuda_time": 1.208636123790453, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.903, + "pct_cuda_time": 1.208636123790453, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.91, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.13045312726656186, + "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": 0.13045312726656186, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 143.943, + "cuda_time_us": 43.647, + "pct_cuda_time": 0.6287420103581742, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.647, + "pct_cuda_time": 0.6287420103581742, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2389.261, + "cuda_time_us": 202.875, + "pct_cuda_time": 2.922446797063134, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.089, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04563554628759585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04563554628759585, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1701.934, + "cuda_time_us": 59.581999999999994, + "pct_cuda_time": 0.8582882319783889, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 161.343, + "cuda_time_us": 22.24, + "pct_cuda_time": 0.32037075424120315, + "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": 0.32037075424120315, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 506.04, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05301098811185377, + "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": 0.05301098811185377, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 710.213, + "cuda_time_us": 17.695, + "pct_cuda_time": 0.2548993028911012, + "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": 0.03457238355120898, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.631, + "pct_cuda_time": 0.19635673341105397, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.023970185928838223, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 173.143, + "cuda_time_us": 15.967, + "pct_cuda_time": 0.23000718673423073, + "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": 15.967, + "pct_cuda_time": 0.23000718673423073, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.938, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04563554628759585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04563554628759585, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 452.841, + "cuda_time_us": 136.957, + "pct_cuda_time": 1.9728874725095533, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 146.049, + "cuda_time_us": 83.038, + "pct_cuda_time": 1.1961756605522047, + "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": 83.038, + "pct_cuda_time": 1.1961756605522047, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.257, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.12953119703852967, + "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": 0.12953119703852967, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 143.616, + "cuda_time_us": 44.927, + "pct_cuda_time": 0.6471806149188191, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.927, + "pct_cuda_time": 0.6471806149188191, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2641.604, + "cuda_time_us": 201.024, + "pct_cuda_time": 2.895782846249264, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.598, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04655747651562809, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04655747651562809, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1871.112, + "cuda_time_us": 58.336000000000006, + "pct_cuda_time": 0.8403394028513863, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.208, + "cuda_time_us": 20.704, + "pct_cuda_time": 0.2982444287684295, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.704, + "pct_cuda_time": 0.2982444287684295, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 500.05, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05301098811185377, + "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": 0.05301098811185377, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 855.063, + "cuda_time_us": 17.376, + "pct_cuda_time": 0.25030405691075297, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.03549431377924122, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.6, + "pct_cuda_time": 0.19591017345685088, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01889956967466091, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 231.153, + "cuda_time_us": 16.576, + "pct_cuda_time": 0.23877992906035, + "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": 0.23877992906035, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 94.722, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04609651140161197, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04609651140161197, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.967, + "cuda_time_us": 136.256, + "pct_cuda_time": 1.9627894554806378, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.285, + "cuda_time_us": 83.679, + "pct_cuda_time": 1.20540936799234, + "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": 83.679, + "pct_cuda_time": 1.20540936799234, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.512, + "cuda_time_us": 8.993, + "pct_cuda_time": 0.12954560219834266, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.993, + "pct_cuda_time": 0.12954560219834266, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.905, + "cuda_time_us": 43.584, + "pct_cuda_time": 0.627834485289955, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.584, + "pct_cuda_time": 0.627834485289955, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2338.409, + "cuda_time_us": 202.94, + "pct_cuda_time": 2.9233831324509794, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.126, + "cuda_time_us": 3.071, + "pct_cuda_time": 0.04423824578573449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.071, + "pct_cuda_time": 0.04423824578573449, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1687.298, + "cuda_time_us": 60.128, + "pct_cuda_time": 0.8661534492362889, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.739, + "cuda_time_us": 22.304, + "pct_cuda_time": 0.32129268446923537, + "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": 0.32129268446923537, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 502.562, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.053932918339886005, + "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": 0.053932918339886005, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 720.61, + "cuda_time_us": 17.569, + "pct_cuda_time": 0.25308425275466273, + "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": 2.593, + "pct_cuda_time": 0.0373525793951187, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.664, + "pct_cuda_time": 0.1968321036848831, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01889956967466091, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 173.601, + "cuda_time_us": 16.511, + "pct_cuda_time": 0.23784359367250477, + "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": 16.511, + "pct_cuda_time": 0.23784359367250477, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.365, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.044713616059563616, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044713616059563616, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 426.415, + "cuda_time_us": 136.637, + "pct_cuda_time": 1.968277821369392, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 140.692, + "cuda_time_us": 83.775, + "pct_cuda_time": 1.2067922633343884, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.775, + "pct_cuda_time": 1.2067922633343884, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.272, + "cuda_time_us": 8.927, + "pct_cuda_time": 0.1285948616506844, + "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": 8.927, + "pct_cuda_time": 0.1285948616506844, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 137.69, + "cuda_time_us": 43.935, + "pct_cuda_time": 0.6328906963843194, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.935, + "pct_cuda_time": 0.6328906963843194, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2270.736, + "cuda_time_us": 200.957, + "pct_cuda_time": 2.8948177005417923, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.034, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04609651140161197, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04609651140161197, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1626.745, + "cuda_time_us": 57.918, + "pct_cuda_time": 0.8343180460495506, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.464, + "cuda_time_us": 20.767, + "pct_cuda_time": 0.29915195383664867, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.767, + "pct_cuda_time": 0.29915195383664867, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 486.279, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05255002299783764, + "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": 0.05255002299783764, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 684.873, + "cuda_time_us": 17.439, + "pct_cuda_time": 0.25121158197897225, + "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": 2.463, + "pct_cuda_time": 0.035479908619428215, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.504, + "pct_cuda_time": 0.1945272781148025, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.021204395244741506, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 170.947, + "cuda_time_us": 16.064, + "pct_cuda_time": 0.23140448723609208, + "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": 16.064, + "pct_cuda_time": 0.23140448723609208, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.478, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.044728021219376614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044728021219376614, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 425.3, + "cuda_time_us": 136.734, + "pct_cuda_time": 1.9696751218712536, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 136.361, + "cuda_time_us": 84.287, + "pct_cuda_time": 1.2141677051586464, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.287, + "pct_cuda_time": 1.2141677051586464, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.134, + "cuda_time_us": 8.736, + "pct_cuda_time": 0.12584347612640068, + "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": 8.736, + "pct_cuda_time": 0.12584347612640068, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.084, + "cuda_time_us": 43.711, + "pct_cuda_time": 0.6296639405862064, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.711, + "pct_cuda_time": 0.6296639405862064, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2191.85, + "cuda_time_us": 202.142, + "pct_cuda_time": 2.911887814920202, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.059, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04563554628759585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04563554628759585, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1559.125, + "cuda_time_us": 58.334999999999994, + "pct_cuda_time": 0.840324997691573, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 131.222, + "cuda_time_us": 21.408, + "pct_cuda_time": 0.30838566127678413, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.408, + "pct_cuda_time": 0.30838566127678413, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 450.56, + "cuda_time_us": 3.839, + "pct_cuda_time": 0.055301408522121356, + "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": 3.839, + "pct_cuda_time": 0.055301408522121356, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 671.255, + "cuda_time_us": 17.471999999999998, + "pct_cuda_time": 0.2516869522528013, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.495, + "pct_cuda_time": 0.03594087373344434, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.472, + "pct_cuda_time": 0.1940663130007864, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.021679765518570628, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 164.27, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.2249509756398664, + "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": 15.616, + "pct_cuda_time": 0.2249509756398664, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.018, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.044713616059563616, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044713616059563616, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 423.781, + "cuda_time_us": 137.535, + "pct_cuda_time": 1.9812136548814692, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 141.568, + "cuda_time_us": 82.847, + "pct_cuda_time": 1.1934242750279207, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 82.847, + "pct_cuda_time": 1.1934242750279207, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 92.874, + "cuda_time_us": 9.248, + "pct_cuda_time": 0.1332189179506586, + "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": 0.1332189179506586, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.252, + "cuda_time_us": 45.44, + "pct_cuda_time": 0.6545704619028899, + "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": 45.44, + "pct_cuda_time": 0.6545704619028899, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2435.556, + "cuda_time_us": 202.204, + "pct_cuda_time": 2.9127809348286084, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.192, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04655747651562809, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04655747651562809, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1788.186, + "cuda_time_us": 58.942, + "pct_cuda_time": 0.8490689296980665, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 233.964, + "cuda_time_us": 21.696, + "pct_cuda_time": 0.31253434730292917, + "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": 0.31253434730292917, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 506.885, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.053932918339886005, + "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": 0.053932918339886005, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 721.988, + "cuda_time_us": 17.151, + "pct_cuda_time": 0.24706289595282713, + "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": 0.03457238355120898, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.471, + "pct_cuda_time": 0.1940519078409734, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01843860456064479, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 170.91, + "cuda_time_us": 16.351, + "pct_cuda_time": 0.23553876810242413, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.351, + "pct_cuda_time": 0.23553876810242413, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.95, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04517458117357973, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04517458117357973, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 426.236, + "cuda_time_us": 136.894, + "pct_cuda_time": 1.9719799474413342, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 145.909, + "cuda_time_us": 83.871, + "pct_cuda_time": 1.2081751586764367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.871, + "pct_cuda_time": 1.2081751586764367, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.859, + "cuda_time_us": 9.376, + "pct_cuda_time": 0.13506277840672307, + "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": 9.376, + "pct_cuda_time": 0.13506277840672307, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 134.085, + "cuda_time_us": 43.647, + "pct_cuda_time": 0.6287420103581742, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.647, + "pct_cuda_time": 0.6287420103581742, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2260.279, + "cuda_time_us": 201.438, + "pct_cuda_time": 2.9017465824118474, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.342, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04425265094554749, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04425265094554749, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1625.698, + "cuda_time_us": 58.943, + "pct_cuda_time": 0.8490833348578795, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.34, + "cuda_time_us": 21.536, + "pct_cuda_time": 0.31022952173284857, + "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": 21.536, + "pct_cuda_time": 0.31022952173284857, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 469.645, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.054854848567918245, + "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": 0.054854848567918245, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 684.321, + "cuda_time_us": 17.727, + "pct_cuda_time": 0.2553602680051173, + "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": 2.655, + "pct_cuda_time": 0.03824569930352493, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.792, + "pct_cuda_time": 0.19867596414094757, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01843860456064479, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 188.755, + "cuda_time_us": 15.872, + "pct_cuda_time": 0.22863869655199537, + "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": 15.872, + "pct_cuda_time": 0.22863869655199537, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.091, + "cuda_time_us": 3.041, + "pct_cuda_time": 0.043806090991344374, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.041, + "pct_cuda_time": 0.043806090991344374, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 417.453, + "cuda_time_us": 136.382, + "pct_cuda_time": 1.9646045056170762, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 139.64, + "cuda_time_us": 83.839, + "pct_cuda_time": 1.2077141935624207, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.839, + "pct_cuda_time": 1.2077141935624207, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.981, + "cuda_time_us": 8.704, + "pct_cuda_time": 0.12538251101238457, + "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": 8.704, + "pct_cuda_time": 0.12538251101238457, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 131.911, + "cuda_time_us": 43.839, + "pct_cuda_time": 0.631507801042271, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.839, + "pct_cuda_time": 0.631507801042271, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2329.038, + "cuda_time_us": 200.38, + "pct_cuda_time": 2.8865059233296892, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.208, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04563554628759585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04563554628759585, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1682.008, + "cuda_time_us": 57.822, + "pct_cuda_time": 0.8329351507075023, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 132.568, + "cuda_time_us": 20.608, + "pct_cuda_time": 0.2968615334263811, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.608, + "pct_cuda_time": 0.2968615334263811, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 453.457, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05208905788382153, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05208905788382153, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 682.74, + "cuda_time_us": 17.663, + "pct_cuda_time": 0.2544383377770851, + "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": 0.035033348665225096, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.728, + "pct_cuda_time": 0.19775403391291535, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.02165095519894462, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 177.638, + "cuda_time_us": 15.935, + "pct_cuda_time": 0.22954622162021462, + "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": 15.935, + "pct_cuda_time": 0.22954622162021462, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.033, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04333072071751525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04333072071751525, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 431.694, + "cuda_time_us": 136.382, + "pct_cuda_time": 1.9646045056170762, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 146.324, + "cuda_time_us": 82.975, + "pct_cuda_time": 1.1952681354839854, + "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": 82.975, + "pct_cuda_time": 1.1952681354839854, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.286, + "cuda_time_us": 9.216, + "pct_cuda_time": 0.13275795283664246, + "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": 0.13275795283664246, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.448, + "cuda_time_us": 44.191, + "pct_cuda_time": 0.6365784172964484, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.191, + "pct_cuda_time": 0.6365784172964484, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2558.88, + "cuda_time_us": 202.302, + "pct_cuda_time": 2.9141926404902825, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.394, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04563554628759585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04563554628759585, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1902.126, + "cuda_time_us": 58.655, + "pct_cuda_time": 0.8449346488317345, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 133.547, + "cuda_time_us": 21.567, + "pct_cuda_time": 0.3106760816870517, + "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": 21.567, + "pct_cuda_time": 0.3106760816870517, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 504.281, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.05439388345390212, + "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": 0.05439388345390212, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 939.352, + "cuda_time_us": 17.632, + "pct_cuda_time": 0.253991777822882, + "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": 0.035033348665225096, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.568, + "pct_cuda_time": 0.19544920834283475, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.023509220814822103, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 185.814, + "cuda_time_us": 15.68, + "pct_cuda_time": 0.22587290586789868, + "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": 15.68, + "pct_cuda_time": 0.22587290586789868, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.198, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.044728021219376614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044728021219376614, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 428.246, + "cuda_time_us": 137.374, + "pct_cuda_time": 1.9788944241515758, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 142.507, + "cuda_time_us": 84.254, + "pct_cuda_time": 1.2136923348848172, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.254, + "pct_cuda_time": 1.2136923348848172, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.059, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.12907023192451353, + "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": 0.12907023192451353, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 133.68, + "cuda_time_us": 44.16, + "pct_cuda_time": 0.6361318573422451, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.16, + "pct_cuda_time": 0.6361318573422451, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2243.149, + "cuda_time_us": 199.83599999999998, + "pct_cuda_time": 2.8786695163914153, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.282, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04517458117357973, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04517458117357973, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1609.443, + "cuda_time_us": 57.56699999999999, + "pct_cuda_time": 0.8292618349551863, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 133.949, + "cuda_time_us": 20.896, + "pct_cuda_time": 0.3010102194525262, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.896, + "pct_cuda_time": 0.3010102194525262, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 475.728, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.05347195322586988, + "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": 0.05347195322586988, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 692.002, + "cuda_time_us": 17.439, + "pct_cuda_time": 0.25121158197897225, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.03549431377924122, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.663, + "pct_cuda_time": 0.1968176985250701, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01889956967466091, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 163.563, + "cuda_time_us": 15.52, + "pct_cuda_time": 0.22356808029781802, + "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": 15.52, + "pct_cuda_time": 0.22356808029781802, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.458, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.043791685831531375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.043791685831531375, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 419.097, + "cuda_time_us": 136.093, + "pct_cuda_time": 1.9604414144311177, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 137.947, + "cuda_time_us": 82.847, + "pct_cuda_time": 1.1934242750279207, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 82.847, + "pct_cuda_time": 1.1934242750279207, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.039, + "cuda_time_us": 8.895, + "pct_cuda_time": 0.12813389653666826, + "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": 8.895, + "pct_cuda_time": 0.12813389653666826, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 137.284, + "cuda_time_us": 44.351, + "pct_cuda_time": 0.6388832428665289, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.351, + "pct_cuda_time": 0.6388832428665289, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2390.209, + "cuda_time_us": 201.79199999999997, + "pct_cuda_time": 2.9068460089856507, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.783, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.044713616059563616, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044713616059563616, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1722.368, + "cuda_time_us": 58.688, + "pct_cuda_time": 0.8454100191055636, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 192.874, + "cuda_time_us": 21.792, + "pct_cuda_time": 0.3139172426449775, + "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": 0.3139172426449775, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 480.935, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.053932918339886005, + "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": 0.053932918339886005, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 716.946, + "cuda_time_us": 17.408, + "pct_cuda_time": 0.25076502202476914, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.03549431377924122, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.632, + "pct_cuda_time": 0.19637113857086697, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01889956967466091, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 189.06, + "cuda_time_us": 15.744, + "pct_cuda_time": 0.2267948360959309, + "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": 15.744, + "pct_cuda_time": 0.2267948360959309, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.409, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.044728021219376614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044728021219376614, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 445.431, + "cuda_time_us": 136.89499999999998, + "pct_cuda_time": 1.9719943526011467, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 146.494, + "cuda_time_us": 83.487, + "pct_cuda_time": 1.2026435773082431, + "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": 83.487, + "pct_cuda_time": 1.2026435773082431, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.913, + "cuda_time_us": 8.833, + "pct_cuda_time": 0.12724077662826205, + "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": 8.833, + "pct_cuda_time": 0.12724077662826205, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.947, + "cuda_time_us": 44.575, + "pct_cuda_time": 0.6421099986646418, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.575, + "pct_cuda_time": 0.6421099986646418, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2302.587, + "cuda_time_us": 200.54000000000002, + "pct_cuda_time": 2.8888107488997705, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.674, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04517458117357973, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04517458117357973, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1594.534, + "cuda_time_us": 57.919, + "pct_cuda_time": 0.8343324512093636, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 135.326, + "cuda_time_us": 20.575, + "pct_cuda_time": 0.29638616315255195, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.575, + "pct_cuda_time": 0.29638616315255195, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 476.586, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05255002299783764, + "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": 0.05255002299783764, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 676.666, + "cuda_time_us": 17.696, + "pct_cuda_time": 0.25491370805091423, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.03549431377924122, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.728, + "pct_cuda_time": 0.19775403391291535, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.021665360358757626, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 167.139, + "cuda_time_us": 16.0, + "pct_cuda_time": 0.23048255700805986, + "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": 16.0, + "pct_cuda_time": 0.23048255700805986, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 104.326, + "cuda_time_us": 3.071, + "pct_cuda_time": 0.04423824578573449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.071, + "pct_cuda_time": 0.04423824578573449, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 453.888, + "cuda_time_us": 136.41400000000002, + "pct_cuda_time": 1.9650654707310926, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 146.888, + "cuda_time_us": 83.519, + "pct_cuda_time": 1.2031045424222595, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.519, + "pct_cuda_time": 1.2031045424222595, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.468, + "cuda_time_us": 8.896, + "pct_cuda_time": 0.12814830169648128, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.896, + "pct_cuda_time": 0.12814830169648128, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 149.503, + "cuda_time_us": 43.999, + "pct_cuda_time": 0.6338126266123516, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.999, + "pct_cuda_time": 0.6338126266123516, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2364.886, + "cuda_time_us": 201.436, + "pct_cuda_time": 2.901717772092222, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.421, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04563554628759585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04563554628759585, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1715.748, + "cuda_time_us": 59.07, + "pct_cuda_time": 0.850912790154131, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 140.314, + "cuda_time_us": 21.248, + "pct_cuda_time": 0.3060808357067035, + "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": 21.248, + "pct_cuda_time": 0.3060808357067035, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 523.166, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05301098811185377, + "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": 0.05301098811185377, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 730.34, + "cuda_time_us": 17.535, + "pct_cuda_time": 0.2525944773210206, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.495, + "pct_cuda_time": 0.03594087373344434, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.536, + "pct_cuda_time": 0.19498824322881864, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.021665360358757626, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 169.895, + "cuda_time_us": 16.607, + "pct_cuda_time": 0.2392264890145531, + "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": 0.2392264890145531, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.625, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04425265094554749, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04425265094554749, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 419.321, + "cuda_time_us": 136.126, + "pct_cuda_time": 1.9609167847049473, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 137.603, + "cuda_time_us": 83.775, + "pct_cuda_time": 1.2067922633343884, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.775, + "pct_cuda_time": 1.2067922633343884, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.573, + "cuda_time_us": 9.023, + "pct_cuda_time": 0.12997775699273276, + "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": 9.023, + "pct_cuda_time": 0.12997775699273276, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 134.276, + "cuda_time_us": 43.328, + "pct_cuda_time": 0.6241467643778261, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.328, + "pct_cuda_time": 0.6241467643778261, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2236.55, + "cuda_time_us": 201.598, + "pct_cuda_time": 2.904051407981928, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.88, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04563554628759585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04563554628759585, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1606.545, + "cuda_time_us": 58.111, + "pct_cuda_time": 0.8370982418934603, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 132.623, + "cuda_time_us": 21.055, + "pct_cuda_time": 0.30330063986279376, + "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": 21.055, + "pct_cuda_time": 0.30330063986279376, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 469.669, + "cuda_time_us": 3.872, + "pct_cuda_time": 0.055776778795950485, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.872, + "pct_cuda_time": 0.055776778795950485, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 698.993, + "cuda_time_us": 17.248, + "pct_cuda_time": 0.24846019645468853, + "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": 0.03457238355120898, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.536, + "pct_cuda_time": 0.19498824322881864, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01889956967466091, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 163.91, + "cuda_time_us": 15.936, + "pct_cuda_time": 0.22956062678002762, + "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": 15.936, + "pct_cuda_time": 0.22956062678002762, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.594, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.042869755603499135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.042869755603499135, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 419.364, + "cuda_time_us": 137.34300000000002, + "pct_cuda_time": 1.9784478641973728, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 135.351, + "cuda_time_us": 84.287, + "pct_cuda_time": 1.2141677051586464, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.287, + "pct_cuda_time": 1.2141677051586464, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 91.383, + "cuda_time_us": 8.864, + "pct_cuda_time": 0.12768733658246517, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.864, + "pct_cuda_time": 0.12768733658246517, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.158, + "cuda_time_us": 44.192, + "pct_cuda_time": 0.6365928224562614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.192, + "pct_cuda_time": 0.6365928224562614, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2205.306, + "cuda_time_us": 202.046, + "pct_cuda_time": 2.9105049195781536, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.515, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04563554628759585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04563554628759585, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1578.528, + "cuda_time_us": 58.719, + "pct_cuda_time": 0.8458565790597666, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 131.587, + "cuda_time_us": 21.695, + "pct_cuda_time": 0.3125199421431162, + "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": 0.3125199421431162, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 467.598, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05531581368193436, + "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": 0.05531581368193436, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 684.489, + "cuda_time_us": 17.568, + "pct_cuda_time": 0.25306984759484974, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.03549431377924122, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.824, + "pct_cuda_time": 0.1991369292549637, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01843860456064479, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 160.739, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.2249509756398664, + "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": 15.616, + "pct_cuda_time": 0.2249509756398664, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.904, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.044713616059563616, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044713616059563616, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 418.196, + "cuda_time_us": 137.055, + "pct_cuda_time": 1.9742991781712276, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 134.362, + "cuda_time_us": 84.095, + "pct_cuda_time": 1.2114019144745496, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.095, + "pct_cuda_time": 1.2114019144745496, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 91.866, + "cuda_time_us": 8.736, + "pct_cuda_time": 0.12584347612640068, + "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": 8.736, + "pct_cuda_time": 0.12584347612640068, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.685, + "cuda_time_us": 44.224, + "pct_cuda_time": 0.6370537875702774, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.224, + "pct_cuda_time": 0.6370537875702774, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2650.709, + "cuda_time_us": 200.797, + "pct_cuda_time": 2.892512874971712, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.642, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04655747651562809, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04655747651562809, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1775.385, + "cuda_time_us": 58.97500000000001, + "pct_cuda_time": 0.8495442999718957, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.851, + "cuda_time_us": 20.927, + "pct_cuda_time": 0.30145677940672927, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.927, + "pct_cuda_time": 0.30145677940672927, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 528.217, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05301098811185377, + "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": 0.05301098811185377, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 795.353, + "cuda_time_us": 17.888, + "pct_cuda_time": 0.25767949873501095, + "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": 0.035033348665225096, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.792, + "pct_cuda_time": 0.19867596414094757, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.023970185928838223, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 181.113, + "cuda_time_us": 16.48, + "pct_cuda_time": 0.23739703371830162, + "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": 0.23739703371830162, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.478, + "cuda_time_us": 3.073, + "pct_cuda_time": 0.04426705610536049, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.073, + "pct_cuda_time": 0.04426705610536049, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 657.621, + "cuda_time_us": 135.517, + "pct_cuda_time": 1.9521440423788279, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 136.355, + "cuda_time_us": 82.814, + "pct_cuda_time": 1.1929489047540918, + "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": 82.814, + "pct_cuda_time": 1.1929489047540918, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.281, + "cuda_time_us": 9.248, + "pct_cuda_time": 0.1332189179506586, + "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": 0.1332189179506586, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 339.902, + "cuda_time_us": 43.455, + "pct_cuda_time": 0.6259762196740775, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.455, + "pct_cuda_time": 0.6259762196740775, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2370.489, + "cuda_time_us": 200.98700000000002, + "pct_cuda_time": 2.8952498553361834, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 100.841, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04563554628759585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04563554628759585, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1674.906, + "cuda_time_us": 58.461, + "pct_cuda_time": 0.8421400478280116, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.032, + "cuda_time_us": 21.215, + "pct_cuda_time": 0.30560546543287437, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.215, + "pct_cuda_time": 0.30560546543287437, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 481.569, + "cuda_time_us": 3.743, + "pct_cuda_time": 0.053918513180073, + "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": 0.053918513180073, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 719.611, + "cuda_time_us": 17.664, + "pct_cuda_time": 0.25445274293689807, + "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": 0.035033348665225096, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.76, + "pct_cuda_time": 0.1982149990269315, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.021204395244741506, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 167.65, + "cuda_time_us": 15.839, + "pct_cuda_time": 0.22816332627816627, + "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": 15.839, + "pct_cuda_time": 0.22816332627816627, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.634, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.042869755603499135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.042869755603499135, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 438.195, + "cuda_time_us": 136.382, + "pct_cuda_time": 1.9646045056170762, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 150.996, + "cuda_time_us": 83.615, + "pct_cuda_time": 1.2044874377643078, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.615, + "pct_cuda_time": 1.2044874377643078, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.609, + "cuda_time_us": 8.864, + "pct_cuda_time": 0.12768733658246517, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.864, + "pct_cuda_time": 0.12768733658246517, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.276, + "cuda_time_us": 43.903, + "pct_cuda_time": 0.6324297312703032, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.903, + "pct_cuda_time": 0.6324297312703032, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2271.026, + "cuda_time_us": 200.349, + "pct_cuda_time": 2.886059363375486, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.05, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04425265094554749, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04425265094554749, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1592.959, + "cuda_time_us": 57.375, + "pct_cuda_time": 0.8264960442710896, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 135.532, + "cuda_time_us": 20.671, + "pct_cuda_time": 0.2977690584946003, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.671, + "pct_cuda_time": 0.2977690584946003, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 462.771, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.053932918339886005, + "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": 0.053932918339886005, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 682.19, + "cuda_time_us": 17.119, + "pct_cuda_time": 0.24660193083881105, + "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": 0.035033348665225096, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.407, + "pct_cuda_time": 0.19312997761294115, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01843860456064479, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 169.448, + "cuda_time_us": 15.841, + "pct_cuda_time": 0.22819213659779225, + "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": 15.841, + "pct_cuda_time": 0.22819213659779225, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.85, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.043791685831531375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.043791685831531375, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 439.248, + "cuda_time_us": 136.862, + "pct_cuda_time": 1.971518982327318, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 140.203, + "cuda_time_us": 83.871, + "pct_cuda_time": 1.2081751586764367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.871, + "pct_cuda_time": 1.2081751586764367, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 111.964, + "cuda_time_us": 9.216, + "pct_cuda_time": 0.13275795283664246, + "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": 0.13275795283664246, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 135.945, + "cuda_time_us": 43.775, + "pct_cuda_time": 0.6305858708142387, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.775, + "pct_cuda_time": 0.6305858708142387, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2358.978, + "cuda_time_us": 202.366, + "pct_cuda_time": 2.9151145707183153, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.114, + "cuda_time_us": 3.135, + "pct_cuda_time": 0.04516017601376673, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.135, + "pct_cuda_time": 0.04516017601376673, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1718.084, + "cuda_time_us": 59.263999999999996, + "pct_cuda_time": 0.8537073911578535, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 142.304, + "cuda_time_us": 21.92, + "pct_cuda_time": 0.315761103101042, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.92, + "pct_cuda_time": 0.315761103101042, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 505.104, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05531581368193436, + "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": 0.05531581368193436, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 744.243, + "cuda_time_us": 17.6, + "pct_cuda_time": 0.25353081270886585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.03549431377924122, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.792, + "pct_cuda_time": 0.19867596414094757, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 1.344, + "pct_cuda_time": 0.01936053478867703, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 186.67, + "cuda_time_us": 15.904, + "pct_cuda_time": 0.22909966166601148, + "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": 15.904, + "pct_cuda_time": 0.22909966166601148, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.374, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.042869755603499135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.042869755603499135, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 418.892, + "cuda_time_us": 136.991, + "pct_cuda_time": 1.9733772479431957, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 138.881, + "cuda_time_us": 83.839, + "pct_cuda_time": 1.2077141935624207, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.839, + "pct_cuda_time": 1.2077141935624207, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.776, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.12907023192451353, + "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": 0.12907023192451353, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 132.502, + "cuda_time_us": 44.192, + "pct_cuda_time": 0.6365928224562614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.192, + "pct_cuda_time": 0.6365928224562614, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2360.581, + "cuda_time_us": 200.32000000000002, + "pct_cuda_time": 2.8856416137409098, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.713, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04609651140161197, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04609651140161197, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1686.818, + "cuda_time_us": 58.04900000000001, + "pct_cuda_time": 0.8362051219850543, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.392, + "cuda_time_us": 20.64, + "pct_cuda_time": 0.2973224985403972, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.64, + "pct_cuda_time": 0.2973224985403972, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 491.727, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05301098811185377, + "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": 0.05301098811185377, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 716.293, + "cuda_time_us": 17.569000000000003, + "pct_cuda_time": 0.2530842527546628, + "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": 0.035047753825038094, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.6, + "pct_cuda_time": 0.19591017345685088, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.022126325472773746, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 190.888, + "cuda_time_us": 16.16, + "pct_cuda_time": 0.23278738257814044, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.16, + "pct_cuda_time": 0.23278738257814044, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.019, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.043791685831531375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.043791685831531375, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 437.636, + "cuda_time_us": 136.031, + "pct_cuda_time": 1.959548294522712, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 150.684, + "cuda_time_us": 83.615, + "pct_cuda_time": 1.2044874377643078, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.615, + "pct_cuda_time": 1.2044874377643078, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.917, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.12999216215254575, + "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": 0.12999216215254575, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.528, + "cuda_time_us": 43.392, + "pct_cuda_time": 0.6250686946058583, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.392, + "pct_cuda_time": 0.6250686946058583, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2335.929, + "cuda_time_us": 202.10899999999998, + "pct_cuda_time": 2.911412444646373, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 91.398, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04563554628759585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04563554628759585, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1667.493, + "cuda_time_us": 59.071, + "pct_cuda_time": 0.8509271953139439, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 140.796, + "cuda_time_us": 21.759, + "pct_cuda_time": 0.3134418723711484, + "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": 21.759, + "pct_cuda_time": 0.3134418723711484, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 514.625, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05255002299783764, + "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": 0.05255002299783764, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 685.252, + "cuda_time_us": 18.016000000000002, + "pct_cuda_time": 0.2595233591910754, + "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": 0.035033348665225096, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 14.08, + "pct_cuda_time": 0.20282465016709267, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.021665360358757626, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 174.875, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.22541194075388254, + "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": 15.648, + "pct_cuda_time": 0.22541194075388254, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.153, + "cuda_time_us": 2.944, + "pct_cuda_time": 0.04240879048948301, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04240879048948301, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 423.448, + "cuda_time_us": 136.926, + "pct_cuda_time": 1.97244091255535, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 139.108, + "cuda_time_us": 83.327, + "pct_cuda_time": 1.2003387517381625, + "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": 1.2003387517381625, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.107, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.13229698772262635, + "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": 0.13229698772262635, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 135.081, + "cuda_time_us": 44.415, + "pct_cuda_time": 0.6398051730945611, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.415, + "pct_cuda_time": 0.6398051730945611, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2295.259, + "cuda_time_us": 200.284, + "pct_cuda_time": 2.885123027987641, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.021, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04563554628759585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04563554628759585, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1635.854, + "cuda_time_us": 58.59, + "pct_cuda_time": 0.8439983134438892, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 140.656, + "cuda_time_us": 21.631, + "pct_cuda_time": 0.31159801191508396, + "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": 0.31159801191508396, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 469.403, + "cuda_time_us": 3.743, + "pct_cuda_time": 0.053918513180073, + "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": 0.053918513180073, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 689.45, + "cuda_time_us": 17.216, + "pct_cuda_time": 0.2479992313406724, + "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": 0.03457238355120898, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.568, + "pct_cuda_time": 0.19544920834283475, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.017977639446628668, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 194.092, + "cuda_time_us": 16.0, + "pct_cuda_time": 0.23048255700805986, + "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": 16.0, + "pct_cuda_time": 0.23048255700805986, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.054, + "cuda_time_us": 2.912, + "pct_cuda_time": 0.04194782537546689, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04194782537546689, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 429.942, + "cuda_time_us": 135.614, + "pct_cuda_time": 1.9535413428806894, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 143.61, + "cuda_time_us": 83.391, + "pct_cuda_time": 1.201260681966195, + "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": 83.391, + "pct_cuda_time": 1.201260681966195, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.593, + "cuda_time_us": 8.832, + "pct_cuda_time": 0.12722637146844903, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.832, + "pct_cuda_time": 0.12722637146844903, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 137.076, + "cuda_time_us": 43.391, + "pct_cuda_time": 0.6250542894460454, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.391, + "pct_cuda_time": 0.6250542894460454, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2265.302, + "cuda_time_us": 201.565, + "pct_cuda_time": 2.903576037708099, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.269, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04563554628759585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04563554628759585, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1621.672, + "cuda_time_us": 58.816, + "pct_cuda_time": 0.8472538795616281, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.212, + "cuda_time_us": 21.792, + "pct_cuda_time": 0.3139172426449775, + "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": 0.3139172426449775, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 494.6, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05301098811185377, + "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": 0.05301098811185377, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 670.24, + "cuda_time_us": 17.472, + "pct_cuda_time": 0.25168695225280135, + "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": 0.035955278893257336, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.664, + "pct_cuda_time": 0.1968321036848831, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01889956967466091, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 164.961, + "cuda_time_us": 15.872, + "pct_cuda_time": 0.22863869655199537, + "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": 15.872, + "pct_cuda_time": 0.22863869655199537, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.247, + "cuda_time_us": 2.975, + "pct_cuda_time": 0.04285535044368613, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.975, + "pct_cuda_time": 0.04285535044368613, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 416.803, + "cuda_time_us": 136.606, + "pct_cuda_time": 1.967831261415189, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 136.896, + "cuda_time_us": 84.127, + "pct_cuda_time": 1.2118628795885658, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.127, + "pct_cuda_time": 1.2118628795885658, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.617, + "cuda_time_us": 8.768, + "pct_cuda_time": 0.12630444124041681, + "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": 8.768, + "pct_cuda_time": 0.12630444124041681, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 133.421, + "cuda_time_us": 43.711, + "pct_cuda_time": 0.6296639405862064, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.711, + "pct_cuda_time": 0.6296639405862064, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2516.316, + "cuda_time_us": 199.678, + "pct_cuda_time": 2.876393501140961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.363, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04563554628759585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04563554628759585, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1872.032, + "cuda_time_us": 57.952, + "pct_cuda_time": 0.8348078214831927, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.101, + "cuda_time_us": 20.768, + "pct_cuda_time": 0.2991663589964617, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.768, + "pct_cuda_time": 0.2991663589964617, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 489.724, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05208905788382153, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05208905788382153, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 926.349, + "cuda_time_us": 17.664, + "pct_cuda_time": 0.25445274293689807, + "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": 0.03687720912128958, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.6, + "pct_cuda_time": 0.19591017345685088, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.021665360358757626, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 181.245, + "cuda_time_us": 15.904, + "pct_cuda_time": 0.22909966166601148, + "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": 15.904, + "pct_cuda_time": 0.22909966166601148, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.165, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04333072071751525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04333072071751525, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 427.508, + "cuda_time_us": 135.55, + "pct_cuda_time": 1.952619412652657, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 148.722, + "cuda_time_us": 83.103, + "pct_cuda_time": 1.1971119959400498, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.103, + "pct_cuda_time": 1.1971119959400498, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.217, + "cuda_time_us": 9.28, + "pct_cuda_time": 0.1336798830646747, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.28, + "pct_cuda_time": 0.1336798830646747, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 133.51, + "cuda_time_us": 43.167, + "pct_cuda_time": 0.6218275336479325, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.167, + "pct_cuda_time": 0.6218275336479325, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2248.951, + "cuda_time_us": 202.20600000000002, + "pct_cuda_time": 2.912809745148235, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.252, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04655747651562809, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04655747651562809, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1596.261, + "cuda_time_us": 59.072, + "pct_cuda_time": 0.850941600473757, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 134.36, + "cuda_time_us": 21.6, + "pct_cuda_time": 0.31115145196088084, + "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": 0.31115145196088084, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 463.805, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.05255002299783764, + "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": 0.05255002299783764, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 684.128, + "cuda_time_us": 17.408, + "pct_cuda_time": 0.25076502202476914, + "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": 0.03457238355120898, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.568, + "pct_cuda_time": 0.19544920834283475, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.020743430130725386, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 174.583, + "cuda_time_us": 16.416, + "pct_cuda_time": 0.2364751034902694, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.416, + "pct_cuda_time": 0.2364751034902694, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.436, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04333072071751525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04333072071751525, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 434.194, + "cuda_time_us": 136.894, + "pct_cuda_time": 1.9719799474413342, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 150.195, + "cuda_time_us": 83.679, + "pct_cuda_time": 1.20540936799234, + "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": 83.679, + "pct_cuda_time": 1.20540936799234, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.196, + "cuda_time_us": 9.568, + "pct_cuda_time": 0.13782856909081978, + "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": 0.13782856909081978, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.93, + "cuda_time_us": 43.647, + "pct_cuda_time": 0.6287420103581742, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.647, + "pct_cuda_time": 0.6287420103581742, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2401.779, + "cuda_time_us": 200.79799999999997, + "pct_cuda_time": 2.892527280131525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.958, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.044713616059563616, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044713616059563616, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1762.378, + "cuda_time_us": 57.375, + "pct_cuda_time": 0.8264960442710896, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 153.734, + "cuda_time_us": 20.735, + "pct_cuda_time": 0.29869098872263256, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.735, + "pct_cuda_time": 0.29869098872263256, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 538.585, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.053932918339886005, + "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": 0.053932918339886005, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 691.912, + "cuda_time_us": 17.216, + "pct_cuda_time": 0.2479992313406724, + "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": 0.03457238355120898, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.504, + "pct_cuda_time": 0.1945272781148025, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.01889956967466091, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 233.939, + "cuda_time_us": 15.68, + "pct_cuda_time": 0.22587290586789868, + "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": 15.68, + "pct_cuda_time": 0.22587290586789868, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.304, + "cuda_time_us": 2.912, + "pct_cuda_time": 0.04194782537546689, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04194782537546689, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 419.763, + "cuda_time_us": 137.40699999999998, + "pct_cuda_time": 1.979369794425405, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 140.224, + "cuda_time_us": 84.095, + "pct_cuda_time": 1.2114019144745496, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.095, + "pct_cuda_time": 1.2114019144745496, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.558, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.12860926681049742, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.12860926681049742, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 133.337, + "cuda_time_us": 44.384, + "pct_cuda_time": 0.639358613140358, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.384, + "pct_cuda_time": 0.639358613140358, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2320.564, + "cuda_time_us": 202.90900000000002, + "pct_cuda_time": 2.922936572496776, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.602, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04563554628759585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04563554628759585, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1605.461, + "cuda_time_us": 59.87100000000001, + "pct_cuda_time": 0.862451323164347, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 134.238, + "cuda_time_us": 21.792, + "pct_cuda_time": 0.3139172426449775, + "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": 0.3139172426449775, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 469.655, + "cuda_time_us": 3.903, + "pct_cuda_time": 0.056223338750153604, + "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": 3.903, + "pct_cuda_time": 0.056223338750153604, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 689.246, + "cuda_time_us": 18.336000000000002, + "pct_cuda_time": 0.26413301033123665, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.656, + "pct_cuda_time": 0.03826010446333794, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 14.4, + "pct_cuda_time": 0.20743430130725388, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01843860456064479, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 164.687, + "cuda_time_us": 15.84, + "pct_cuda_time": 0.22817773143797926, + "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": 15.84, + "pct_cuda_time": 0.22817773143797926, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 142.9, + "cuda_time_us": 2.976, + "pct_cuda_time": 0.042869755603499135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.042869755603499135, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 434.381, + "cuda_time_us": 136.894, + "pct_cuda_time": 1.9719799474413342, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 145.647, + "cuda_time_us": 84.031, + "pct_cuda_time": 1.2104799842465173, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.031, + "pct_cuda_time": 1.2104799842465173, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.237, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.12907023192451353, + "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": 0.12907023192451353, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 139.028, + "cuda_time_us": 43.903, + "pct_cuda_time": 0.6324297312703032, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.903, + "pct_cuda_time": 0.6324297312703032, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2396.961, + "cuda_time_us": 200.79500000000002, + "pct_cuda_time": 2.8924840646520864, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.827, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04655747651562809, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04655747651562809, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1722.351, + "cuda_time_us": 58.334, + "pct_cuda_time": 0.8403105925317603, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 130.97, + "cuda_time_us": 20.736, + "pct_cuda_time": 0.29870539388244555, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.736, + "pct_cuda_time": 0.29870539388244555, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 512.06, + "cuda_time_us": 3.583, + "pct_cuda_time": 0.05161368760999241, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.583, + "pct_cuda_time": 0.05161368760999241, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 734.302, + "cuda_time_us": 17.663, + "pct_cuda_time": 0.2544383377770851, + "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": 0.035033348665225096, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 13.727, + "pct_cuda_time": 0.19773962875310236, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 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": 0.021665360358757626, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 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[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 190.835, + "cuda_time_us": 16.352, + "pct_cuda_time": 0.23555317326223718, + "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": 0.23555317326223718, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.377, + "cuda_time_us": 2.943, + "pct_cuda_time": 0.042394385329670006, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 2.943, + "pct_cuda_time": 0.042394385329670006, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 446.843, + "cuda_time_us": 136.286, + "pct_cuda_time": 1.9632216102750277, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 144.109, + "cuda_time_us": 83.167, + "pct_cuda_time": 1.1980339261680821, + "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": 83.167, + "pct_cuda_time": 1.1980339261680821, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.085, + "cuda_time_us": 8.864, + "pct_cuda_time": 0.12768733658246517, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.864, + "pct_cuda_time": 0.12768733658246517, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.461, + "cuda_time_us": 44.255, + "pct_cuda_time": 0.6375003475244806, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.255, + "pct_cuda_time": 0.6375003475244806, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.38, + "cuda_time_us": 3.328, + "pct_cuda_time": 0.047940371857676446, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.047940371857676446, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 494.938, + "cuda_time_us": 351.42, + "pct_cuda_time": 5.062261261485775, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 7.168, + "pct_cuda_time": 0.10325618553961081, + "trace": "index_select(bfloat16[12, 4096], 0, int64[12])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.010602197622370753, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[12, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 343.516, + "pct_cuda_time": 4.948402878323793, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[12, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 4087.124, + "cuda_time_us": 125.439, + "pct_cuda_time": 1.806968841783376, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.010602197622370753, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 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": 0.010602197622370753, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.011524127850402993, + "trace": "copy_(int32[12], int32[12], True) <- _to_copy(int32[12], 3, 0, None, None, True, None) <- to(int32[12], 3, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.011524127850402993, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 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": 0.011063162736386873, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 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": 0.011063162736386873, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 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": 0.011063162736386873, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 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": 6.784, + "pct_cuda_time": 0.09772460417141737, + "trace": "copy_(float32[12, 128256], bfloat16[12, 128256], False) <- _to_copy(bfloat16[12, 128256], 6, None, None, None, False, None) <- to(bfloat16[12, 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": 8.576, + "pct_cuda_time": 0.12353865055632009, + "trace": "div_(float32[12, 128256], bfloat16[12, 1])" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::SoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.88, + "pct_cuda_time": 0.5024519742775705, + "trace": "_softmax(float32[12, 128256], -1, False) <- softmax(float32[12, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::LogSoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 28.256, + "pct_cuda_time": 0.40703219567623367, + "trace": "_log_softmax(float32[12, 128256], -1, False) <- log_softmax(float32[12, 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": 1.632, + "pct_cuda_time": 0.023509220814822103, + "trace": "copy_(int64[12], int32[12], False) <- _to_copy(int32[12], 4, None, None, None, False, None) <- to(int32[12], 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": 9.344, + "pct_cuda_time": 0.13460181329270693, + "trace": "index(float32[12, 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": 27.999, + "pct_cuda_time": 0.4033300696042917, + "trace": "argmax(float32[12, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 2.592, + "pct_cuda_time": 0.03733817423530569, + "trace": "copy_(int64[12], int64[12], False) <- _to_copy(int64[12], 4, 0, None, None, False, None) <- to(int64[12], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + } +} \ No newline at end of file