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+ "cpu_time_us": 235.109, + "cuda_time_us": 48.992, + "pct_cuda_time": 0.0733197187810453, + "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": 48.992, + "pct_cuda_time": 0.0733197187810453, + "trace": "_C::rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2980.23, + "cuda_time_us": 481.9459999999999, + "pct_cuda_time": 0.7212635774748869, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 396.545, + "cuda_time_us": 216.06099999999998, + "pct_cuda_time": 0.3233493582534175, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 215.325, + "pct_cuda_time": 0.3222478863187578, + "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": 955.362, + "cuda_time_us": 40.544, + "pct_cuda_time": 0.060676736574516256, + "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": 40.544, + "pct_cuda_time": 0.060676736574516256, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1052.888, + "cuda_time_us": 73.183, + "pct_cuda_time": 0.10952312580734078, + "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": 0.024184492478398358, + "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": 55.743, + "pct_cuda_time": 0.0834230299643168, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0019156033646256121, + "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[7], int32[7], None, None, None, 512, 512, 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": 311.571, + "cuda_time_us": 152.158, + "pct_cuda_time": 0.22771435683961241, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 151.422, + "pct_cuda_time": 0.22661288490495268, + "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": 117.137, + "cuda_time_us": 31.232, + "pct_cuda_time": 0.04674072209686494, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04674072209686494, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 602.204, + "cuda_time_us": 1527.914, + "pct_cuda_time": 2.286622811920764, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 196.982, + "cuda_time_us": 956.1469999999999, + "pct_cuda_time": 1.4309362580286602, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 955.411, + "pct_cuda_time": 1.4298347860940006, + "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": 141.342, + "cuda_time_us": 131.806, + "pct_cuda_time": 0.1972562633420652, + "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": 131.806, + "pct_cuda_time": 0.1972562633420652, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 183.235, + "cuda_time_us": 439.961, + "pct_cuda_time": 0.6584302905500383, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0010999753695311132, + "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": 439.226, + "pct_cuda_time": 0.6573303151805072, + "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": 2699.276, + "cuda_time_us": 2066.341, + "pct_cuda_time": 3.0924138844248845, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.124, + "cuda_time_us": 32.448, + "pct_cuda_time": 0.04856054529325927, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04856054529325927, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1932.086, + "cuda_time_us": 475.067, + "pct_cuda_time": 0.7109687059551529, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 168.183, + "cuda_time_us": 209.405, + "pct_cuda_time": 0.31338822075736433, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 208.669, + "pct_cuda_time": 0.31228674882270463, + "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": 539.5, + "cuda_time_us": 40.8, + "pct_cuda_time": 0.06105985724744139, + "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": 0.06105985724744139, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 835.148, + "cuda_time_us": 73.344, + "pct_cuda_time": 0.10976407279304756, + "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": 0.024040822226051434, + "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": 55.775, + "pct_cuda_time": 0.08347092004843243, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.002252330518563708, + "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[7], int32[7], None, None, None, 512, 512, 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": 225.536, + "cuda_time_us": 151.518, + "pct_cuda_time": 0.22675655515729962, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 150.782, + "pct_cuda_time": 0.22565508322263994, + "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": 92.746, + "cuda_time_us": 30.687, + "pct_cuda_time": 0.045925094101770446, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.045925094101770446, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 494.818, + "cuda_time_us": 1528.139, + "pct_cuda_time": 2.2869595390747017, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 167.81, + "cuda_time_us": 956.306, + "pct_cuda_time": 1.43117421188411, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0010999753695311132, + "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": 955.571, + "pct_cuda_time": 1.4300742365145789, + "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": 104.497, + "cuda_time_us": 131.935, + "pct_cuda_time": 0.19744932024365636, + "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": 131.935, + "pct_cuda_time": 0.19744932024365636, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 168.236, + "cuda_time_us": 439.89799999999997, + "pct_cuda_time": 0.6583360069469355, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 439.162, + "pct_cuda_time": 0.6572345350122758, + "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": 2590.836, + "cuda_time_us": 2069.765, + "pct_cuda_time": 3.0975381234252577, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.09, + "cuda_time_us": 32.959, + "pct_cuda_time": 0.049325290073980906, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.049325290073980906, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1841.788, + "cuda_time_us": 478.01, + "pct_cuda_time": 0.7153730971286633, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 161.026, + "cuda_time_us": 211.422, + "pct_cuda_time": 0.3164067926217783, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011029684997883409, + "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": 210.685, + "pct_cuda_time": 0.31530382412198993, + "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": 544.109, + "cuda_time_us": 40.383, + "pct_cuda_time": 0.060435789588809456, + "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": 40.383, + "pct_cuda_time": 0.060435789588809456, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 766.862, + "cuda_time_us": 74.30300000000001, + "pct_cuda_time": 0.1111992787513882, + "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": 16.512, + "pct_cuda_time": 0.024711283403670396, + "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": 56.287, + "pct_cuda_time": 0.0842371613942827, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0022508339534350946, + "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[7], int32[7], None, None, None, 512, 512, 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": 202.956, + "cuda_time_us": 151.902, + "pct_cuda_time": 0.22733123616668727, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 151.166, + "pct_cuda_time": 0.2262297642320276, + "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": 80.809, + "cuda_time_us": 31.551, + "pct_cuda_time": 0.04721812637289273, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.551, + "pct_cuda_time": 0.04721812637289273, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 499.094, + "cuda_time_us": 1527.245, + "pct_cuda_time": 2.285621609849721, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 166.616, + "cuda_time_us": 956.3389999999999, + "pct_cuda_time": 1.4312235985333541, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 955.603, + "pct_cuda_time": 1.4301221265986943, + "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": 100.41, + "cuda_time_us": 131.935, + "pct_cuda_time": 0.19744932024365636, + "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": 131.935, + "pct_cuda_time": 0.19744932024365636, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 177.162, + "cuda_time_us": 438.971, + "pct_cuda_time": 0.6569486910727107, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011029684997883409, + "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": 438.234, + "pct_cuda_time": 0.6558457225729223, + "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": 2490.857, + "cuda_time_us": 2068.4179999999997, + "pct_cuda_time": 3.0955222501970145, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.01, + "cuda_time_us": 32.703, + "pct_cuda_time": 0.048942169401055786, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.048942169401055786, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1781.754, + "cuda_time_us": 477.687, + "pct_cuda_time": 0.714889706592121, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.35, + "cuda_time_us": 211.453, + "pct_cuda_time": 0.3164531861407653, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.888, + "pct_cuda_time": 0.002825514962822778, + "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": 209.565, + "pct_cuda_time": 0.31362767117794255, + "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": 501.376, + "cuda_time_us": 40.608, + "pct_cuda_time": 0.06077251674274754, + "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": 40.608, + "pct_cuda_time": 0.06077251674274754, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 760.559, + "cuda_time_us": 74.077, + "pct_cuda_time": 0.11086105503232146, + "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": 16.447, + "pct_cuda_time": 0.024614006670310502, + "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": 56.351, + "pct_cuda_time": 0.08433294156251396, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 1.279, + "pct_cuda_time": 0.0019141067994969984, + "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[7], int32[7], None, None, None, 512, 512, 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": 202.205, + "cuda_time_us": 151.549, + "pct_cuda_time": 0.22680294867628664, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0010999753695311132, + "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": 150.814, + "pct_cuda_time": 0.22570297330675554, + "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": 93.533, + "cuda_time_us": 30.4, + "pct_cuda_time": 0.04549557990985829, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04549557990985829, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 474.551, + "cuda_time_us": 1527.6279999999997, + "pct_cuda_time": 2.28619479429398, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 162.595, + "cuda_time_us": 954.675, + "pct_cuda_time": 1.4287333141593408, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 953.939, + "pct_cuda_time": 1.4276318422246812, + "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": 103.53, + "cuda_time_us": 132.447, + "pct_cuda_time": 0.1982155615895066, + "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": 132.447, + "pct_cuda_time": 0.1982155615895066, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.084, + "cuda_time_us": 440.506, + "pct_cuda_time": 0.6592459185451327, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 439.77, + "pct_cuda_time": 0.658144446610473, + "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": 2522.55, + "cuda_time_us": 2069.923, + "pct_cuda_time": 3.097774580715579, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.134, + "cuda_time_us": 33.151, + "pct_cuda_time": 0.049612630578674745, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.049612630578674745, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1804.677, + "cuda_time_us": 475.193, + "pct_cuda_time": 0.7111572731613582, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 153.915, + "cuda_time_us": 209.85299999999998, + "pct_cuda_time": 0.31405868193498326, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 209.117, + "pct_cuda_time": 0.31295721000032356, + "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": 489.406, + "cuda_time_us": 40.319, + "pct_cuda_time": 0.060340009420578176, + "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": 40.319, + "pct_cuda_time": 0.060340009420578176, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 786.038, + "cuda_time_us": 73.11999999999999, + "pct_cuda_time": 0.10942884220423807, + "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": 16.192, + "pct_cuda_time": 0.024232382562513994, + "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": 55.616, + "pct_cuda_time": 0.08323296619298284, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0019634934487412527, + "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[7], int32[7], None, None, None, 512, 512, 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": 221.289, + "cuda_time_us": 151.901, + "pct_cuda_time": 0.22732973960155872, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0010999753695311132, + "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": 151.166, + "pct_cuda_time": 0.2262297642320276, + "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": 90.944, + "cuda_time_us": 31.263, + "pct_cuda_time": 0.04678711561585197, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04678711561585197, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 482.363, + "cuda_time_us": 1530.3159999999998, + "pct_cuda_time": 2.2902175613596936, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 171.315, + "cuda_time_us": 957.524, + "pct_cuda_time": 1.4329970282107616, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 956.756, + "pct_cuda_time": 1.4318476661919861, + "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": 98.761, + "cuda_time_us": 131.422, + "pct_cuda_time": 0.19668158233267752, + "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": 131.422, + "pct_cuda_time": 0.19668158233267752, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 157.467, + "cuda_time_us": 441.37, + "pct_cuda_time": 0.6605389508162551, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 0.6594374788815953, + "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": 2540.129, + "cuda_time_us": 2069.093, + "pct_cuda_time": 3.0965324316588294, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.262, + "cuda_time_us": 32.928, + "pct_cuda_time": 0.04927889655499387, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.928, + "pct_cuda_time": 0.04927889655499387, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1819.521, + "cuda_time_us": 477.146, + "pct_cuda_time": 0.7140800648575409, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.807, + "cuda_time_us": 210.27, + "pct_cuda_time": 0.31468274959361525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 209.502, + "pct_cuda_time": 0.3135333875748399, + "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": 560.75, + "cuda_time_us": 40.639, + "pct_cuda_time": 0.06081891026173458, + "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": 40.639, + "pct_cuda_time": 0.06081891026173458, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 738.995, + "cuda_time_us": 74.366, + "pct_cuda_time": 0.11129356235449084, + "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": 0.024280272646629634, + "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": 56.639, + "pct_cuda_time": 0.08476395231955473, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0022493373883064804, + "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[7], int32[7], None, None, None, 512, 512, 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": 199.662, + "cuda_time_us": 151.87099999999998, + "pct_cuda_time": 0.22728484264770027, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 151.135, + "pct_cuda_time": 0.22618337071304054, + "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": 87.132, + "cuda_time_us": 31.744, + "pct_cuda_time": 0.04750696344271518, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.744, + "pct_cuda_time": 0.04750696344271518, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 468.315, + "cuda_time_us": 1527.275, + "pct_cuda_time": 2.2856665068035795, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.817, + "cuda_time_us": 956.083, + "pct_cuda_time": 1.4308404778604291, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 955.347, + "pct_cuda_time": 1.4297390059257693, + "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": 101.357, + "cuda_time_us": 131.934, + "pct_cuda_time": 0.19744782367852776, + "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": 131.934, + "pct_cuda_time": 0.19744782367852776, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.599, + "cuda_time_us": 439.258, + "pct_cuda_time": 0.6573782052646228, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 438.522, + "pct_cuda_time": 0.6562767333299631, + "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": 2427.58, + "cuda_time_us": 2067.908, + "pct_cuda_time": 3.094759001981422, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.643, + "cuda_time_us": 32.8, + "pct_cuda_time": 0.049087336218531306, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.8, + "pct_cuda_time": 0.049087336218531306, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1745.797, + "cuda_time_us": 475.64199999999994, + "pct_cuda_time": 0.7118292309041058, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.586, + "cuda_time_us": 209.85299999999998, + "pct_cuda_time": 0.31405868193498326, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 209.117, + "pct_cuda_time": 0.31295721000032356, + "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": 477.632, + "cuda_time_us": 40.511, + "pct_cuda_time": 0.060627349925272016, + "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": 40.511, + "pct_cuda_time": 0.060627349925272016, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 740.879, + "cuda_time_us": 73.63199999999999, + "pct_cuda_time": 0.11019508355008834, + "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": 15.936, + "pct_cuda_time": 0.02384926188958887, + "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": 56.224, + "pct_cuda_time": 0.08414287779118002, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.002202943869319454, + "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[7], int32[7], None, None, None, 512, 512, 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": 190.22, + "cuda_time_us": 151.646, + "pct_cuda_time": 0.22694811549376215, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 150.91, + "pct_cuda_time": 0.22584664355910244, + "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": 77.119, + "cuda_time_us": 31.36, + "pct_cuda_time": 0.0469322824333275, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.36, + "pct_cuda_time": 0.0469322824333275, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 462.532, + "cuda_time_us": 1528.106, + "pct_cuda_time": 2.286910152425458, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.387, + "cuda_time_us": 954.995, + "pct_cuda_time": 1.4292122150004973, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 954.227, + "pct_cuda_time": 1.4280628529817219, + "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": 97.813, + "cuda_time_us": 131.966, + "pct_cuda_time": 0.1974957137626434, + "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": 131.966, + "pct_cuda_time": 0.1974957137626434, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 159.13, + "cuda_time_us": 441.14500000000004, + "pct_cuda_time": 0.660202223662317, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0010999753695311132, + "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": 0.6591022482927859, + "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": 2515.035, + "cuda_time_us": 2072.453, + "pct_cuda_time": 3.1015608904909717, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.358, + "cuda_time_us": 32.576, + "pct_cuda_time": 0.048752105629721834, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.048752105629721834, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1796.55, + "cuda_time_us": 475.994, + "pct_cuda_time": 0.7123560218293779, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 153.311, + "cuda_time_us": 210.461, + "pct_cuda_time": 0.31496859353318046, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 209.693, + "pct_cuda_time": 0.3138192315144051, + "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": 495.87, + "cuda_time_us": 40.192, + "pct_cuda_time": 0.06014994564924423, + "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": 40.192, + "pct_cuda_time": 0.06014994564924423, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 748.813, + "cuda_time_us": 73.40599999999999, + "pct_cuda_time": 0.10985685983102161, + "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": 16.095, + "pct_cuda_time": 0.02408721574503846, + "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": 55.999, + "pct_cuda_time": 0.08380615063724192, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0019634934487412527, + "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[7], int32[7], None, None, None, 512, 512, 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": 250.364, + "cuda_time_us": 151.935, + "pct_cuda_time": 0.22738062281593158, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011029684997883409, + "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": 151.198, + "pct_cuda_time": 0.22627765431614322, + "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": 87.233, + "cuda_time_us": 31.2, + "pct_cuda_time": 0.0466928320127493, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.0466928320127493, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 488.006, + "cuda_time_us": 1532.683, + "pct_cuda_time": 2.293759931019123, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 170.228, + "cuda_time_us": 958.739, + "pct_cuda_time": 1.4348153548420273, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 958.003, + "pct_cuda_time": 1.4337138829073675, + "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": 102.373, + "cuda_time_us": 131.486, + "pct_cuda_time": 0.19677736250090877, + "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": 131.486, + "pct_cuda_time": 0.19677736250090877, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 158.231, + "cuda_time_us": 442.45799999999997, + "pct_cuda_time": 0.6621672136761868, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 441.722, + "pct_cuda_time": 0.6610657417415271, + "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": 2426.288, + "cuda_time_us": 2067.5879999999997, + "pct_cuda_time": 3.0942801011402654, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.07, + "cuda_time_us": 32.544, + "pct_cuda_time": 0.04870421554560619, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04870421554560619, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1728.691, + "cuda_time_us": 474.96999999999997, + "pct_cuda_time": 0.7108235391376773, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 160.392, + "cuda_time_us": 209.501, + "pct_cuda_time": 0.31353189100971124, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 208.733, + "pct_cuda_time": 0.3123825289909359, + "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": 486.102, + "cuda_time_us": 40.575, + "pct_cuda_time": 0.0607231300935033, + "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": 40.575, + "pct_cuda_time": 0.0607231300935033, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 737.911, + "cuda_time_us": 73.28, + "pct_cuda_time": 0.10966829262481631, + "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": 16.672, + "pct_cuda_time": 0.0249507338242486, + "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": 55.328, + "pct_cuda_time": 0.0828019554359421, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0019156033646256121, + "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[7], int32[7], None, None, None, 512, 512, 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": 195.924, + "cuda_time_us": 151.61399999999998, + "pct_cuda_time": 0.22690022540964652, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 150.878, + "pct_cuda_time": 0.2257987534749868, + "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": 79.312, + "cuda_time_us": 31.327, + "pct_cuda_time": 0.04688289578408325, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.327, + "pct_cuda_time": 0.04688289578408325, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.242, + "cuda_time_us": 1528.7469999999998, + "pct_cuda_time": 2.287869450672899, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 161.356, + "cuda_time_us": 956.3389999999999, + "pct_cuda_time": 1.4312235985333541, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 955.603, + "pct_cuda_time": 1.4301221265986943, + "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": 96.492, + "cuda_time_us": 132.03, + "pct_cuda_time": 0.19759149393087466, + "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": 132.03, + "pct_cuda_time": 0.19759149393087466, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 163.321, + "cuda_time_us": 440.378, + "pct_cuda_time": 0.6590543582086702, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 439.642, + "pct_cuda_time": 0.6579528862740105, + "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": 2430.033, + "cuda_time_us": 2068.131, + "pct_cuda_time": 3.095092736005103, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.845, + "cuda_time_us": 32.736, + "pct_cuda_time": 0.04899155605030003, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04899155605030003, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1723.108, + "cuda_time_us": 476.825, + "pct_cuda_time": 0.7135996674512559, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 149.543, + "cuda_time_us": 210.109, + "pct_cuda_time": 0.31444180260790844, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 209.341, + "pct_cuda_time": 0.3132924405891331, + "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": 482.436, + "cuda_time_us": 40.767, + "pct_cuda_time": 0.06101047059819714, + "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": 40.767, + "pct_cuda_time": 0.06101047059819714, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 732.941, + "cuda_time_us": 73.759, + "pct_cuda_time": 0.1103851473214223, + "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": 0.024280272646629634, + "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": 56.031, + "pct_cuda_time": 0.08385404072135756, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0022508339534350946, + "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[7], int32[7], None, None, None, 512, 512, 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": 211.865, + "cuda_time_us": 152.19, + "pct_cuda_time": 0.22776224692372804, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 151.422, + "pct_cuda_time": 0.22661288490495268, + "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": 86.882, + "cuda_time_us": 30.847, + "pct_cuda_time": 0.046164544522348645, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.847, + "pct_cuda_time": 0.046164544522348645, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 473.485, + "cuda_time_us": 1527.723, + "pct_cuda_time": 2.2863369679811987, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.519, + "cuda_time_us": 957.011, + "pct_cuda_time": 1.4322292902997826, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 956.275, + "pct_cuda_time": 1.4311278183651228, + "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": 100.642, + "cuda_time_us": 131.358, + "pct_cuda_time": 0.19658580216444624, + "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": 131.358, + "pct_cuda_time": 0.19658580216444624, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.605, + "cuda_time_us": 439.354, + "pct_cuda_time": 0.6575218755169696, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 438.618, + "pct_cuda_time": 0.65642040358231, + "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": 2464.363, + "cuda_time_us": 2072.4869999999996, + "pct_cuda_time": 3.1016117737053444, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.364, + "cuda_time_us": 32.864, + "pct_cuda_time": 0.04918311638676259, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.864, + "pct_cuda_time": 0.04918311638676259, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1760.907, + "cuda_time_us": 476.66499999999996, + "pct_cuda_time": 0.7133602170306776, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 158.101, + "cuda_time_us": 211.069, + "pct_cuda_time": 0.3158785051313776, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 210.301, + "pct_cuda_time": 0.31472914311260225, + "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": 530.616, + "cuda_time_us": 40.575, + "pct_cuda_time": 0.0607231300935033, + "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": 40.575, + "pct_cuda_time": 0.0607231300935033, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 714.53, + "cuda_time_us": 73.63000000000001, + "pct_cuda_time": 0.11019209041983112, + "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": 16.256, + "pct_cuda_time": 0.024328162730745277, + "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": 55.871, + "pct_cuda_time": 0.08361459030077936, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0022493373883064804, + "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[7], int32[7], None, None, None, 512, 512, 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": 193.855, + "cuda_time_us": 151.391, + "pct_cuda_time": 0.22656649138596566, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011029684997883409, + "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": 150.654, + "pct_cuda_time": 0.22546352288617733, + "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": 78.02, + "cuda_time_us": 30.912, + "pct_cuda_time": 0.04626182125570853, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.912, + "pct_cuda_time": 0.04626182125570853, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 479.275, + "cuda_time_us": 1532.0459999999998, + "pct_cuda_time": 2.292806619032196, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.391, + "cuda_time_us": 959.2529999999999, + "pct_cuda_time": 1.4355845893181345, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011029684997883409, + "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": 958.516, + "pct_cuda_time": 1.4344816208183464, + "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": 95.641, + "cuda_time_us": 131.519, + "pct_cuda_time": 0.19682674915015305, + "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": 131.519, + "pct_cuda_time": 0.19682674915015305, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.492, + "cuda_time_us": 441.274, + "pct_cuda_time": 0.6603952805639082, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 440.538, + "pct_cuda_time": 0.6592938086292484, + "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": 2407.236, + "cuda_time_us": 2068.2619999999997, + "pct_cuda_time": 3.095288786036951, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.718, + "cuda_time_us": 32.384, + "pct_cuda_time": 0.04846476512502799, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.384, + "pct_cuda_time": 0.04846476512502799, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1738.319, + "cuda_time_us": 476.025, + "pct_cuda_time": 0.7124024153483649, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 152.847, + "cuda_time_us": 209.661, + "pct_cuda_time": 0.31377134143028945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 208.925, + "pct_cuda_time": 0.3126698694956297, + "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": 487.839, + "cuda_time_us": 41.088, + "pct_cuda_time": 0.06149086800448215, + "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": 41.088, + "pct_cuda_time": 0.06149086800448215, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 714.92, + "cuda_time_us": 73.75899999999999, + "pct_cuda_time": 0.11038514732142227, + "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": 0.024040822226051434, + "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": 56.351, + "pct_cuda_time": 0.08433294156251396, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.002011383532856893, + "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[7], int32[7], None, None, None, 512, 512, 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": 187.725, + "cuda_time_us": 151.51700000000002, + "pct_cuda_time": 0.22675505859217104, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0010999753695311132, + "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": 150.782, + "pct_cuda_time": 0.22565508322263994, + "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": 76.699, + "cuda_time_us": 30.88, + "pct_cuda_time": 0.04621393117159289, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04621393117159289, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 453.435, + "cuda_time_us": 1528.973, + "pct_cuda_time": 2.2882076743919657, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 155.975, + "cuda_time_us": 956.98, + "pct_cuda_time": 1.4321828967807957, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 956.244, + "pct_cuda_time": 1.4310814248461359, + "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": 95.751, + "cuda_time_us": 131.742, + "pct_cuda_time": 0.1971604831738339, + "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": 131.742, + "pct_cuda_time": 0.1971604831738339, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.631, + "cuda_time_us": 440.251, + "pct_cuda_time": 0.6588642944373362, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 439.515, + "pct_cuda_time": 0.6577628225026765, + "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": 2547.896, + "cuda_time_us": 2066.691, + "pct_cuda_time": 3.0929376822198993, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.042, + "cuda_time_us": 32.575, + "pct_cuda_time": 0.04875060906459323, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.575, + "pct_cuda_time": 0.04875060906459323, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1852.305, + "cuda_time_us": 475.385, + "pct_cuda_time": 0.711444613666052, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.744, + "cuda_time_us": 209.50099999999998, + "pct_cuda_time": 0.3135318910097112, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 208.765, + "pct_cuda_time": 0.3124304190750515, + "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": 499.642, + "cuda_time_us": 40.255, + "pct_cuda_time": 0.06024422925234689, + "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": 40.255, + "pct_cuda_time": 0.06024422925234689, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 835.483, + "cuda_time_us": 73.759, + "pct_cuda_time": 0.1103851473214223, + "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": 0.024136602394282714, + "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": 56.159, + "pct_cuda_time": 0.08404560105782012, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.002202943869319454, + "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[7], int32[7], None, None, None, 512, 512, 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": 211.666, + "cuda_time_us": 151.87, + "pct_cuda_time": 0.22728334608257167, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 151.102, + "pct_cuda_time": 0.2261339840637963, + "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": 80.278, + "cuda_time_us": 30.72, + "pct_cuda_time": 0.04597448075101469, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.72, + "pct_cuda_time": 0.04597448075101469, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 475.715, + "cuda_time_us": 1528.011, + "pct_cuda_time": 2.2867679787382396, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.528, + "cuda_time_us": 956.723, + "pct_cuda_time": 1.4317982795427417, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 955.987, + "pct_cuda_time": 1.4306968076080822, + "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": 100.283, + "cuda_time_us": 131.55, + "pct_cuda_time": 0.1968731426691401, + "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": 131.55, + "pct_cuda_time": 0.1968731426691401, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 157.756, + "cuda_time_us": 439.738, + "pct_cuda_time": 0.6580965565263575, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 439.002, + "pct_cuda_time": 0.6569950845916976, + "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": 2357.085, + "cuda_time_us": 2068.899, + "pct_cuda_time": 3.0962420980238785, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.973, + "cuda_time_us": 32.192, + "pct_cuda_time": 0.04817742462033415, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.192, + "pct_cuda_time": 0.04817742462033415, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1691.596, + "cuda_time_us": 475.769, + "pct_cuda_time": 0.7120192946754398, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.31, + "cuda_time_us": 209.949, + "pct_cuda_time": 0.3142023521873302, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 209.181, + "pct_cuda_time": 0.31305299016855487, + "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": 488.363, + "cuda_time_us": 40.223, + "pct_cuda_time": 0.06019633916823125, + "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": 0.06019633916823125, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 719.778, + "cuda_time_us": 74.111, + "pct_cuda_time": 0.11091193824669433, + "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": 0.024280272646629634, + "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": 56.383, + "pct_cuda_time": 0.08438083164662961, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0022508339534350946, + "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[7], int32[7], None, None, None, 512, 512, 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": 187.883, + "cuda_time_us": 151.486, + "pct_cuda_time": 0.22670866507318396, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 150.75, + "pct_cuda_time": 0.22560719313852426, + "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": 76.868, + "cuda_time_us": 30.751, + "pct_cuda_time": 0.046020874270001726, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.751, + "pct_cuda_time": 0.046020874270001726, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 447.692, + "cuda_time_us": 1530.187, + "pct_cuda_time": 2.2900245044581027, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 155.768, + "cuda_time_us": 959.251, + "pct_cuda_time": 1.4355815961878773, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 958.515, + "pct_cuda_time": 1.4344801242532177, + "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": 95.392, + "cuda_time_us": 131.454, + "pct_cuda_time": 0.19672947241679314, + "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": 131.454, + "pct_cuda_time": 0.19672947241679314, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 147.925, + "cuda_time_us": 439.48199999999997, + "pct_cuda_time": 0.6577134358534322, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 438.714, + "pct_cuda_time": 0.6565640738346569, + "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": 2409.957, + "cuda_time_us": 2070.8509999999997, + "pct_cuda_time": 3.099163393154932, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.224, + "cuda_time_us": 33.951, + "pct_cuda_time": 0.05080988268156575, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.951, + "pct_cuda_time": 0.05080988268156575, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1716.139, + "cuda_time_us": 476.057, + "pct_cuda_time": 0.7124503054324806, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 174.122, + "cuda_time_us": 209.564, + "pct_cuda_time": 0.3136261746128139, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 208.828, + "pct_cuda_time": 0.3125247026781542, + "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": 462.663, + "cuda_time_us": 40.128, + "pct_cuda_time": 0.060054165481012944, + "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": 40.128, + "pct_cuda_time": 0.060054165481012944, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 725.479, + "cuda_time_us": 74.335, + "pct_cuda_time": 0.1112471688355038, + "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": 16.032, + "pct_cuda_time": 0.02399293214193579, + "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": 56.831, + "pct_cuda_time": 0.08505129282424857, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.002202943869319454, + "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[7], int32[7], None, None, None, 512, 512, 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": 205.735, + "cuda_time_us": 152.03, + "pct_cuda_time": 0.22752279650314985, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 151.294, + "pct_cuda_time": 0.22642132456849015, + "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": 85.43, + "cuda_time_us": 30.944, + "pct_cuda_time": 0.04630971133982417, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04630971133982417, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 467.696, + "cuda_time_us": 1529.899, + "pct_cuda_time": 2.289593493701062, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.004, + "cuda_time_us": 958.067, + "pct_cuda_time": 1.4338096630755988, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 957.331, + "pct_cuda_time": 1.4327081911409392, + "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": 98.596, + "cuda_time_us": 131.55, + "pct_cuda_time": 0.1968731426691401, + "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": 131.55, + "pct_cuda_time": 0.1968731426691401, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 153.826, + "cuda_time_us": 440.282, + "pct_cuda_time": 0.6589106879563233, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 439.546, + "pct_cuda_time": 0.6578092160216635, + "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": 2427.016, + "cuda_time_us": 2069.06, + "pct_cuda_time": 3.096483045009585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.931, + "cuda_time_us": 33.056, + "pct_cuda_time": 0.04947045689145643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.056, + "pct_cuda_time": 0.04947045689145643, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1733.511, + "cuda_time_us": 474.586, + "pct_cuda_time": 0.7102488581282898, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 152.654, + "cuda_time_us": 209.69299999999998, + "pct_cuda_time": 0.31381923151440505, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 208.957, + "pct_cuda_time": 0.31271775957974535, + "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": 515.737, + "cuda_time_us": 40.383, + "pct_cuda_time": 0.060435789588809456, + "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": 40.383, + "pct_cuda_time": 0.060435789588809456, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 701.518, + "cuda_time_us": 73.18299999999999, + "pct_cuda_time": 0.10952312580734076, + "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": 0.023897151973704515, + "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": 55.903, + "pct_cuda_time": 0.083662480384895, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0019634934487412527, + "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[7], int32[7], None, None, None, 512, 512, 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": 213.178, + "cuda_time_us": 151.327, + "pct_cuda_time": 0.22647071121773438, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011029684997883409, + "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": 150.59, + "pct_cuda_time": 0.22536774271794605, + "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": 83.643, + "cuda_time_us": 31.871, + "pct_cuda_time": 0.04769702721404913, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.871, + "pct_cuda_time": 0.04769702721404913, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 464.59, + "cuda_time_us": 1529.547, + "pct_cuda_time": 2.28906670277579, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.928, + "cuda_time_us": 957.491, + "pct_cuda_time": 1.4329476415615172, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 956.755, + "pct_cuda_time": 1.4318461696268576, + "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": 96.873, + "cuda_time_us": 131.518, + "pct_cuda_time": 0.19682525258502442, + "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": 131.518, + "pct_cuda_time": 0.19682525258502442, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 153.256, + "cuda_time_us": 440.53799999999995, + "pct_cuda_time": 0.6592938086292484, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 439.77, + "pct_cuda_time": 0.658144446610473, + "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": 2351.505, + "cuda_time_us": 2073.891, + "pct_cuda_time": 3.1037129511459183, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.626, + "cuda_time_us": 33.664, + "pct_cuda_time": 0.050380368489653604, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.664, + "pct_cuda_time": 0.050380368489653604, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1664.769, + "cuda_time_us": 475.32099999999997, + "pct_cuda_time": 0.7113488334978207, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 153.175, + "cuda_time_us": 210.045, + "pct_cuda_time": 0.3143460224396771, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 209.309, + "pct_cuda_time": 0.31324455050501737, + "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": 474.527, + "cuda_time_us": 40.223, + "pct_cuda_time": 0.06019633916823125, + "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": 0.06019633916823125, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 709.827, + "cuda_time_us": 73.567, + "pct_cuda_time": 0.11009780681672844, + "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": 0.02394504205782015, + "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": 56.255, + "pct_cuda_time": 0.08418927131016705, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0019634934487412527, + "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[7], int32[7], None, None, None, 512, 512, 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": 184.422, + "cuda_time_us": 151.486, + "pct_cuda_time": 0.22670866507318396, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 150.75, + "pct_cuda_time": 0.22560719313852426, + "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": 78.07, + "cuda_time_us": 30.847, + "pct_cuda_time": 0.046164544522348645, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.847, + "pct_cuda_time": 0.046164544522348645, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 458.773, + "cuda_time_us": 1534.059, + "pct_cuda_time": 2.295819204636095, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.81, + "cuda_time_us": 961.427, + "pct_cuda_time": 1.438838121907741, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 960.659, + "pct_cuda_time": 1.4376887598889656, + "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": 98.153, + "cuda_time_us": 131.806, + "pct_cuda_time": 0.1972562633420652, + "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": 131.806, + "pct_cuda_time": 0.1972562633420652, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.923, + "cuda_time_us": 440.82599999999996, + "pct_cuda_time": 0.6597248193862891, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 440.09, + "pct_cuda_time": 0.6586233474516294, + "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": 2439.666, + "cuda_time_us": 2068.4539999999997, + "pct_cuda_time": 3.095576126541645, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.419, + "cuda_time_us": 33.12, + "pct_cuda_time": 0.04956623705968771, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04956623705968771, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1724.515, + "cuda_time_us": 474.84299999999996, + "pct_cuda_time": 0.7106334753663434, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.995, + "cuda_time_us": 209.534, + "pct_cuda_time": 0.3135812776589555, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 208.798, + "pct_cuda_time": 0.31247980572429573, + "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": 471.188, + "cuda_time_us": 40.319, + "pct_cuda_time": 0.060340009420578176, + "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": 40.319, + "pct_cuda_time": 0.060340009420578176, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 743.292, + "cuda_time_us": 73.183, + "pct_cuda_time": 0.10952312580734078, + "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": 0.024040822226051434, + "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": 55.487, + "pct_cuda_time": 0.08303990929139168, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0024423942898976554, + "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[7], int32[7], None, None, None, 512, 512, 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": 214.126, + "cuda_time_us": 151.807, + "pct_cuda_time": 0.227189062479469, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011029684997883409, + "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": 0.22608609397968063, + "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": 85.95, + "cuda_time_us": 30.72, + "pct_cuda_time": 0.04597448075101469, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.72, + "pct_cuda_time": 0.04597448075101469, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 480.278, + "cuda_time_us": 1529.771, + "pct_cuda_time": 2.2894019333645996, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 168.151, + "cuda_time_us": 958.259, + "pct_cuda_time": 1.4340970035802927, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 957.523, + "pct_cuda_time": 1.432995531645633, + "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": 102.455, + "cuda_time_us": 131.454, + "pct_cuda_time": 0.19672947241679314, + "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": 131.454, + "pct_cuda_time": 0.19672947241679314, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 157.015, + "cuda_time_us": 440.058, + "pct_cuda_time": 0.6585754573675138, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 439.322, + "pct_cuda_time": 0.6574739854328541, + "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": 2396.779, + "cuda_time_us": 2070.244, + "pct_cuda_time": 3.0982549781218642, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.529, + "cuda_time_us": 32.8, + "pct_cuda_time": 0.049087336218531306, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.8, + "pct_cuda_time": 0.049087336218531306, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1706.2, + "cuda_time_us": 477.24199999999996, + "pct_cuda_time": 0.7142237351098878, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 157.676, + "cuda_time_us": 210.91, + "pct_cuda_time": 0.315640551275928, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 210.142, + "pct_cuda_time": 0.31449118925715264, + "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": 485.961, + "cuda_time_us": 41.087, + "pct_cuda_time": 0.06148937143935355, + "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": 41.087, + "pct_cuda_time": 0.06148937143935355, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 715.189, + "cuda_time_us": 74.111, + "pct_cuda_time": 0.11091193824669433, + "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": 16.288, + "pct_cuda_time": 0.024376052814860917, + "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": 56.319, + "pct_cuda_time": 0.08428505147839832, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0022508339534350946, + "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[7], int32[7], None, None, None, 512, 512, 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": 195.171, + "cuda_time_us": 151.134, + "pct_cuda_time": 0.2261818741479119, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 150.398, + "pct_cuda_time": 0.22508040221325218, + "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": 82.868, + "cuda_time_us": 31.135, + "pct_cuda_time": 0.046595555279389404, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.135, + "pct_cuda_time": 0.046595555279389404, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 462.512, + "cuda_time_us": 1529.067, + "pct_cuda_time": 2.2883483515140557, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.538, + "cuda_time_us": 956.787, + "pct_cuda_time": 1.4318940597109733, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 956.051, + "pct_cuda_time": 1.4307925877763135, + "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": 98.854, + "cuda_time_us": 131.55, + "pct_cuda_time": 0.1968731426691401, + "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": 131.55, + "pct_cuda_time": 0.1968731426691401, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 147.96, + "cuda_time_us": 440.73, + "pct_cuda_time": 0.6595811491339423, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 439.994, + "pct_cuda_time": 0.6584796771992826, + "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": 2487.746, + "cuda_time_us": 2074.148, + "pct_cuda_time": 3.104097568383972, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.048, + "cuda_time_us": 33.28, + "pct_cuda_time": 0.04980568748026592, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.28, + "pct_cuda_time": 0.04980568748026592, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1779.334, + "cuda_time_us": 475.51300000000003, + "pct_cuda_time": 0.7116361740025147, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 154.644, + "cuda_time_us": 210.20499999999998, + "pct_cuda_time": 0.3145854728602553, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 209.469, + "pct_cuda_time": 0.3134840009255956, + "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": 467.222, + "cuda_time_us": 40.096, + "pct_cuda_time": 0.060006275396897304, + "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": 40.096, + "pct_cuda_time": 0.060006275396897304, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 795.194, + "cuda_time_us": 73.247, + "pct_cuda_time": 0.10961890597557206, + "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": 0.02394504205782015, + "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": 55.967, + "pct_cuda_time": 0.08375826055312628, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0019156033646256121, + "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[7], int32[7], None, None, None, 512, 512, 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": 215.276, + "cuda_time_us": 151.965, + "pct_cuda_time": 0.22742551976979, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0010999753695311132, + "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": 151.23, + "pct_cuda_time": 0.22632554440025887, + "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": 85.866, + "cuda_time_us": 31.584, + "pct_cuda_time": 0.047267513022136984, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.584, + "pct_cuda_time": 0.047267513022136984, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 476.82, + "cuda_time_us": 1533.771, + "pct_cuda_time": 2.2953881938790546, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.664, + "cuda_time_us": 961.107, + "pct_cuda_time": 1.4383592210665848, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 960.371, + "pct_cuda_time": 1.437257749131925, + "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": 100.542, + "cuda_time_us": 131.422, + "pct_cuda_time": 0.19668158233267752, + "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": 131.422, + "pct_cuda_time": 0.19668158233267752, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.377, + "cuda_time_us": 441.24199999999996, + "pct_cuda_time": 0.6603473904797924, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 440.506, + "pct_cuda_time": 0.6592459185451327, + "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": 2488.81, + "cuda_time_us": 2066.404, + "pct_cuda_time": 3.092508168027987, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.598, + "cuda_time_us": 32.703, + "pct_cuda_time": 0.048942169401055786, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.048942169401055786, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1798.055, + "cuda_time_us": 476.442, + "pct_cuda_time": 0.7130264830069968, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.883, + "cuda_time_us": 210.493, + "pct_cuda_time": 0.31501648361729606, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 209.757, + "pct_cuda_time": 0.31391501168263636, + "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": 522.225, + "cuda_time_us": 41.375, + "pct_cuda_time": 0.0619203821963943, + "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": 41.375, + "pct_cuda_time": 0.0619203821963943, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 757.309, + "cuda_time_us": 73.08699999999999, + "pct_cuda_time": 0.10937945555499383, + "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": 0.024184492478398358, + "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": 55.583, + "pct_cuda_time": 0.08318357954373859, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.002011383532856893, + "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[7], int32[7], None, None, None, 512, 512, 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": 209.219, + "cuda_time_us": 151.487, + "pct_cuda_time": 0.22671016163831262, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011029684997883409, + "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": 150.75, + "pct_cuda_time": 0.22560719313852426, + "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": 82.443, + "cuda_time_us": 30.944, + "pct_cuda_time": 0.04630971133982417, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04630971133982417, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 461.971, + "cuda_time_us": 1526.315, + "pct_cuda_time": 2.2842298042801104, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.547, + "cuda_time_us": 955.411, + "pct_cuda_time": 1.4298347860940006, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 954.675, + "pct_cuda_time": 1.4287333141593408, + "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": 97.272, + "cuda_time_us": 130.974, + "pct_cuda_time": 0.19601112115505853, + "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": 130.974, + "pct_cuda_time": 0.19601112115505853, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.051, + "cuda_time_us": 439.93, + "pct_cuda_time": 0.6583838970310513, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 439.194, + "pct_cuda_time": 0.6572824250963916, + "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": 2344.98, + "cuda_time_us": 2065.636, + "pct_cuda_time": 3.091358806009212, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.001, + "cuda_time_us": 32.352, + "pct_cuda_time": 0.04841687504091235, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04841687504091235, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1678.804, + "cuda_time_us": 474.07399999999996, + "pct_cuda_time": 0.7094826167824394, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 158.868, + "cuda_time_us": 209.661, + "pct_cuda_time": 0.31377134143028945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 208.893, + "pct_cuda_time": 0.3126219794115141, + "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": 482.907, + "cuda_time_us": 40.64, + "pct_cuda_time": 0.06082040682686318, + "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": 40.64, + "pct_cuda_time": 0.06082040682686318, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 708.3, + "cuda_time_us": 72.991, + "pct_cuda_time": 0.10923578530264694, + "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": 16.192, + "pct_cuda_time": 0.024232382562513994, + "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": 55.327, + "pct_cuda_time": 0.08280045887081347, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.002202943869319454, + "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[7], int32[7], None, None, None, 512, 512, 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": 183.835, + "cuda_time_us": 150.78199999999998, + "pct_cuda_time": 0.22565508322263989, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 150.046, + "pct_cuda_time": 0.22455361128798015, + "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": 75.234, + "cuda_time_us": 31.103, + "pct_cuda_time": 0.046547665195273764, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.103, + "pct_cuda_time": 0.046547665195273764, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 444.156, + "cuda_time_us": 1528.107, + "pct_cuda_time": 2.286911648990586, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 156.45, + "cuda_time_us": 956.915, + "pct_cuda_time": 1.4320856200474357, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 956.179, + "pct_cuda_time": 1.4309841481127759, + "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": 92.863, + "cuda_time_us": 131.294, + "pct_cuda_time": 0.19649002199621496, + "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": 131.294, + "pct_cuda_time": 0.19649002199621496, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 145.532, + "cuda_time_us": 439.89799999999997, + "pct_cuda_time": 0.6583360069469355, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 439.13, + "pct_cuda_time": 0.6571866449281603, + "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": 2494.754, + "cuda_time_us": 2066.7569999999996, + "pct_cuda_time": 3.093036455518387, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.744, + "cuda_time_us": 32.512, + "pct_cuda_time": 0.048656325461490554, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.512, + "pct_cuda_time": 0.048656325461490554, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1784.061, + "cuda_time_us": 473.178, + "pct_cuda_time": 0.7081416944272014, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.852, + "cuda_time_us": 209.981, + "pct_cuda_time": 0.3142502422714458, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 209.213, + "pct_cuda_time": 0.31310088025267047, + "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": 517.777, + "cuda_time_us": 40.351, + "pct_cuda_time": 0.060387899504693816, + "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": 40.351, + "pct_cuda_time": 0.060387899504693816, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 737.743, + "cuda_time_us": 72.54299999999999, + "pct_cuda_time": 0.10856532412502795, + "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": 16.192, + "pct_cuda_time": 0.024232382562513994, + "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": 54.879, + "pct_cuda_time": 0.08212999769319451, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.002202943869319454, + "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[7], int32[7], None, None, None, 512, 512, 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": 225.714, + "cuda_time_us": 150.303, + "pct_cuda_time": 0.2249382285260339, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 149.567, + "pct_cuda_time": 0.2238367565913742, + "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": 88.8, + "cuda_time_us": 30.463, + "pct_cuda_time": 0.04558986351296096, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04558986351296096, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 476.366, + "cuda_time_us": 1530.6039999999998, + "pct_cuda_time": 2.2906485721167344, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 167.63, + "cuda_time_us": 959.251, + "pct_cuda_time": 1.4355815961878773, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 958.515, + "pct_cuda_time": 1.4344801242532177, + "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": 99.726, + "cuda_time_us": 131.743, + "pct_cuda_time": 0.19716197973896252, + "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": 131.743, + "pct_cuda_time": 0.19716197973896252, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.239, + "cuda_time_us": 439.61, + "pct_cuda_time": 0.6579049961898948, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011029684997883409, + "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": 438.873, + "pct_cuda_time": 0.6568020276901064, + "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": 2430.102, + "cuda_time_us": 2064.5159999999996, + "pct_cuda_time": 3.089682653065164, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.602, + "cuda_time_us": 32.8, + "pct_cuda_time": 0.049087336218531306, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.8, + "pct_cuda_time": 0.049087336218531306, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1716.123, + "cuda_time_us": 475.065, + "pct_cuda_time": 0.7109657128248956, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 159.359, + "cuda_time_us": 209.885, + "pct_cuda_time": 0.3141065720190989, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 209.149, + "pct_cuda_time": 0.31300510008443916, + "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": 480.901, + "cuda_time_us": 40.608, + "pct_cuda_time": 0.06077251674274754, + "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": 40.608, + "pct_cuda_time": 0.06077251674274754, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 737.111, + "cuda_time_us": 73.31, + "pct_cuda_time": 0.10971318957867472, + "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": 15.967, + "pct_cuda_time": 0.023895655408575897, + "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": 56.063, + "pct_cuda_time": 0.08390193080547322, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0019156033646256121, + "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[7], int32[7], None, None, None, 512, 512, 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": 192.747, + "cuda_time_us": 151.262, + "pct_cuda_time": 0.2263734344843745, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 150.494, + "pct_cuda_time": 0.22522407246559914, + "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": 95.689, + "cuda_time_us": 30.464, + "pct_cuda_time": 0.04559136007808957, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.464, + "pct_cuda_time": 0.04559136007808957, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 466.362, + "cuda_time_us": 1526.187, + "pct_cuda_time": 2.2840382439436477, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 161.986, + "cuda_time_us": 955.347, + "pct_cuda_time": 1.4297390059257693, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 954.611, + "pct_cuda_time": 1.4286375339911097, + "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": 94.797, + "cuda_time_us": 131.614, + "pct_cuda_time": 0.19696892283737139, + "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": 131.614, + "pct_cuda_time": 0.19696892283737139, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 156.164, + "cuda_time_us": 439.226, + "pct_cuda_time": 0.6573303151805072, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 438.49, + "pct_cuda_time": 0.6562288432458475, + "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": 2484.31, + "cuda_time_us": 2065.6369999999997, + "pct_cuda_time": 3.09136030257434, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.786, + "cuda_time_us": 32.832, + "pct_cuda_time": 0.04913522630264695, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.832, + "pct_cuda_time": 0.04913522630264695, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1759.334, + "cuda_time_us": 474.52, + "pct_cuda_time": 0.7101500848298012, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 154.336, + "cuda_time_us": 209.437, + "pct_cuda_time": 0.31343611084148, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 208.669, + "pct_cuda_time": 0.31228674882270463, + "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": 490.574, + "cuda_time_us": 40.799, + "pct_cuda_time": 0.06105836068231277, + "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": 40.799, + "pct_cuda_time": 0.06105836068231277, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 748.295, + "cuda_time_us": 72.734, + "pct_cuda_time": 0.10885116806459319, + "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": 15.871, + "pct_cuda_time": 0.023751985156228977, + "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": 55.455, + "pct_cuda_time": 0.08299201920727603, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.002107163701088173, + "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[7], int32[7], None, None, None, 512, 512, 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": 214.912, + "cuda_time_us": 151.54999999999998, + "pct_cuda_time": 0.22680444524141524, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 150.814, + "pct_cuda_time": 0.22570297330675554, + "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": 88.17, + "cuda_time_us": 30.784, + "pct_cuda_time": 0.04607026091924597, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.784, + "pct_cuda_time": 0.04607026091924597, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 479.901, + "cuda_time_us": 1527.501, + "pct_cuda_time": 2.2860047305226465, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.416, + "cuda_time_us": 955.732, + "pct_cuda_time": 1.4303151835002856, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011029684997883409, + "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": 954.995, + "pct_cuda_time": 1.4292122150004973, + "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": 100.474, + "cuda_time_us": 131.454, + "pct_cuda_time": 0.19672947241679314, + "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": 131.454, + "pct_cuda_time": 0.19672947241679314, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 160.752, + "cuda_time_us": 440.315, + "pct_cuda_time": 0.6589600746055676, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 439.547, + "pct_cuda_time": 0.6578107125867921, + "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": 2416.052, + "cuda_time_us": 2068.131, + "pct_cuda_time": 3.095092736005103, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.37, + "cuda_time_us": 32.319, + "pct_cuda_time": 0.048367488391668094, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.319, + "pct_cuda_time": 0.048367488391668094, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1723.126, + "cuda_time_us": 475.193, + "pct_cuda_time": 0.7111572731613582, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 154.367, + "cuda_time_us": 210.46099999999998, + "pct_cuda_time": 0.3149685935331804, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 209.725, + "pct_cuda_time": 0.3138671215985207, + "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": 477.13, + "cuda_time_us": 40.959, + "pct_cuda_time": 0.06129781110289098, + "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": 0.06129781110289098, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 742.899, + "cuda_time_us": 72.383, + "pct_cuda_time": 0.10832587370444977, + "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": 0.024040822226051434, + "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": 54.815, + "pct_cuda_time": 0.08203421752496322, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0022508339534350946, + "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[7], int32[7], None, None, None, 512, 512, 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": 200.799, + "cuda_time_us": 151.39, + "pct_cuda_time": 0.22656499482083706, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 150.654, + "pct_cuda_time": 0.22546352288617733, + "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": 81.138, + "cuda_time_us": 30.944, + "pct_cuda_time": 0.04630971133982417, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04630971133982417, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 461.076, + "cuda_time_us": 1529.675, + "pct_cuda_time": 2.2892582631122527, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.526, + "cuda_time_us": 958.611, + "pct_cuda_time": 1.4346237945055647, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 957.875, + "pct_cuda_time": 1.433522322570905, + "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": 97.731, + "cuda_time_us": 131.198, + "pct_cuda_time": 0.19634635174386803, + "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": 131.198, + "pct_cuda_time": 0.19634635174386803, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 153.151, + "cuda_time_us": 439.866, + "pct_cuda_time": 0.65828811686282, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 439.13, + "pct_cuda_time": 0.6571866449281603, + "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": 2580.16, + "cuda_time_us": 2065.5389999999998, + "pct_cuda_time": 3.0912136391917358, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.949, + "cuda_time_us": 33.184, + "pct_cuda_time": 0.049662017227919, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.184, + "pct_cuda_time": 0.049662017227919, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1809.15, + "cuda_time_us": 475.03200000000004, + "pct_cuda_time": 0.7109163261756515, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 153.931, + "cuda_time_us": 210.141, + "pct_cuda_time": 0.31448969269202404, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 209.373, + "pct_cuda_time": 0.3133403306732486, + "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": 485.8, + "cuda_time_us": 40.447, + "pct_cuda_time": 0.06053156975704074, + "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": 40.447, + "pct_cuda_time": 0.06053156975704074, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 795.454, + "cuda_time_us": 73.69500000000001, + "pct_cuda_time": 0.11028936715319101, + "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": 16.576, + "pct_cuda_time": 0.02480706357190168, + "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": 55.615, + "pct_cuda_time": 0.08323146962785424, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0022508339534350946, + "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[7], int32[7], None, None, None, 512, 512, 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": 220.498, + "cuda_time_us": 150.74900000000002, + "pct_cuda_time": 0.22560569657339566, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0010999753695311132, + "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": 150.014, + "pct_cuda_time": 0.22450572120386453, + "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": 88.467, + "cuda_time_us": 30.464, + "pct_cuda_time": 0.04559136007808957, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.464, + "pct_cuda_time": 0.04559136007808957, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 535.839, + "cuda_time_us": 1526.859, + "pct_cuda_time": 2.285043935710076, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.999, + "cuda_time_us": 955.699, + "pct_cuda_time": 1.4302657968510415, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 954.963, + "pct_cuda_time": 1.4291643249163817, + "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": 100.452, + "cuda_time_us": 131.166, + "pct_cuda_time": 0.1962984616597524, + "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": 0.1962984616597524, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 217.006, + "cuda_time_us": 439.99399999999997, + "pct_cuda_time": 0.6584796771992825, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 439.226, + "pct_cuda_time": 0.6573303151805072, + "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": 2415.92, + "cuda_time_us": 2066.823, + "pct_cuda_time": 3.0931352288168763, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.008, + "cuda_time_us": 32.352, + "pct_cuda_time": 0.04841687504091235, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04841687504091235, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1719.243, + "cuda_time_us": 475.09799999999996, + "pct_cuda_time": 0.7110150994741399, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 159.923, + "cuda_time_us": 210.653, + "pct_cuda_time": 0.3152559340378743, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 209.917, + "pct_cuda_time": 0.31415446210321457, + "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": 479.187, + "cuda_time_us": 40.512, + "pct_cuda_time": 0.06062884649040063, + "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": 0.06062884649040063, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 731.319, + "cuda_time_us": 73.343, + "pct_cuda_time": 0.10976257622791898, + "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": 0.024184492478398358, + "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": 55.903, + "pct_cuda_time": 0.083662480384895, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0019156033646256121, + "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[7], int32[7], None, None, None, 512, 512, 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": 194.399, + "cuda_time_us": 150.59, + "pct_cuda_time": 0.22536774271794605, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 149.854, + "pct_cuda_time": 0.22426627078328634, + "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": 77.547, + "cuda_time_us": 31.392, + "pct_cuda_time": 0.04698017251744314, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.392, + "pct_cuda_time": 0.04698017251744314, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 465.767, + "cuda_time_us": 1527.981, + "pct_cuda_time": 2.286723081784381, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.98, + "cuda_time_us": 956.1469999999999, + "pct_cuda_time": 1.4309362580286602, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 955.411, + "pct_cuda_time": 1.4298347860940006, + "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": 96.018, + "cuda_time_us": 131.646, + "pct_cuda_time": 0.19701681292148698, + "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": 131.646, + "pct_cuda_time": 0.19701681292148698, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.915, + "cuda_time_us": 440.18800000000005, + "pct_cuda_time": 0.6587700108342337, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011029684997883409, + "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": 439.451, + "pct_cuda_time": 0.6576670423344453, + "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": 2367.587, + "cuda_time_us": 2064.997, + "pct_cuda_time": 3.0904025008920275, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.846, + "cuda_time_us": 33.471, + "pct_cuda_time": 0.050091531419831144, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.050091531419831144, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1690.774, + "cuda_time_us": 473.08399999999995, + "pct_cuda_time": 0.7080010173051117, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.459, + "cuda_time_us": 209.34099999999998, + "pct_cuda_time": 0.313292440589133, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 208.605, + "pct_cuda_time": 0.31219096865447327, + "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": 455.707, + "cuda_time_us": 40.0, + "pct_cuda_time": 0.05986260514455038, + "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": 40.0, + "pct_cuda_time": 0.05986260514455038, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 734.639, + "cuda_time_us": 72.833, + "pct_cuda_time": 0.10899932801232595, + "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": 15.937, + "pct_cuda_time": 0.023850758454717486, + "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": 55.584, + "pct_cuda_time": 0.08318507610886722, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0019634934487412527, + "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[7], int32[7], None, None, None, 512, 512, 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": 194.071, + "cuda_time_us": 150.91, + "pct_cuda_time": 0.22584664355910244, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 150.174, + "pct_cuda_time": 0.2247451716244427, + "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": 76.979, + "cuda_time_us": 30.719, + "pct_cuda_time": 0.04597298418588608, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.719, + "pct_cuda_time": 0.04597298418588608, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 461.309, + "cuda_time_us": 1527.723, + "pct_cuda_time": 2.2863369679811987, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.971, + "cuda_time_us": 957.011, + "pct_cuda_time": 1.4322292902997826, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 956.275, + "pct_cuda_time": 1.4311278183651228, + "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": 95.746, + "cuda_time_us": 131.102, + "pct_cuda_time": 0.19620268149152112, + "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": 131.102, + "pct_cuda_time": 0.19620268149152112, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 152.084, + "cuda_time_us": 439.61, + "pct_cuda_time": 0.6579049961898948, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 438.874, + "pct_cuda_time": 0.6568035242552351, + "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": 2448.846, + "cuda_time_us": 2067.844, + "pct_cuda_time": 3.094663221813191, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.518, + "cuda_time_us": 33.056, + "pct_cuda_time": 0.04947045689145643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 33.056, + "pct_cuda_time": 0.04947045689145643, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1754.183, + "cuda_time_us": 473.40099999999995, + "pct_cuda_time": 0.7084754284508823, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 152.557, + "cuda_time_us": 209.117, + "pct_cuda_time": 0.31295721000032356, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 208.349, + "pct_cuda_time": 0.3118078479815482, + "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": 479.8, + "cuda_time_us": 40.447, + "pct_cuda_time": 0.06053156975704074, + "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": 40.447, + "pct_cuda_time": 0.06053156975704074, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 765.181, + "cuda_time_us": 73.53500000000001, + "pct_cuda_time": 0.11004991673261283, + "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": 16.192, + "pct_cuda_time": 0.024232382562513994, + "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": 55.839, + "pct_cuda_time": 0.08356670021666372, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.0022508339534350946, + "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[7], int32[7], None, None, None, 512, 512, 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": 201.728, + "cuda_time_us": 150.302, + "pct_cuda_time": 0.22493673196090527, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 149.566, + "pct_cuda_time": 0.22383526002624554, + "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": 85.392, + "cuda_time_us": 30.879, + "pct_cuda_time": 0.046212434606464285, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.879, + "pct_cuda_time": 0.046212434606464285, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 465.8, + "cuda_time_us": 1530.508, + "pct_cuda_time": 2.290504901864388, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.088, + "cuda_time_us": 958.676, + "pct_cuda_time": 1.4347210712389247, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 957.94, + "pct_cuda_time": 1.4336195993042649, + "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": 100.069, + "cuda_time_us": 131.838, + "pct_cuda_time": 0.19730415342618085, + "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": 0.19730415342618085, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 153.687, + "cuda_time_us": 439.99399999999997, + "pct_cuda_time": 0.6584796771992825, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 439.258, + "pct_cuda_time": 0.6573782052646228, + "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": 2369.895, + "cuda_time_us": 2067.301, + "pct_cuda_time": 3.0938505869483537, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.567, + "cuda_time_us": 32.735, + "pct_cuda_time": 0.04899005948517141, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 32.735, + "pct_cuda_time": 0.04899005948517141, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1689.769, + "cuda_time_us": 474.00800000000004, + "pct_cuda_time": 0.709383843483951, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 152.225, + "cuda_time_us": 209.917, + "pct_cuda_time": 0.31415446210321457, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0010999753695311132, + "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": 209.182, + "pct_cuda_time": 0.3130544867336834, + "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": 484.534, + "cuda_time_us": 40.415, + "pct_cuda_time": 0.06048367967292509, + "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": 40.415, + "pct_cuda_time": 0.06048367967292509, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 721.372, + "cuda_time_us": 73.247, + "pct_cuda_time": 0.10961890597557206, + "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": 16.032, + "pct_cuda_time": 0.02399293214193579, + "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": 55.743, + "pct_cuda_time": 0.0834230299643168, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.002202943869319454, + "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[7], int32[7], None, None, None, 512, 512, 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": 188.706, + "cuda_time_us": 150.429, + "pct_cuda_time": 0.22512679573223923, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0010999753695311132, + "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": 149.694, + "pct_cuda_time": 0.2240268203627081, + "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": 81.908, + "cuda_time_us": 31.456, + "pct_cuda_time": 0.047075952685674424, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 31.456, + "pct_cuda_time": 0.047075952685674424, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 456.765, + "cuda_time_us": 1529.1019999999999, + "pct_cuda_time": 2.2884007312935566, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.152, + "cuda_time_us": 956.7239999999999, + "pct_cuda_time": 1.4317997761078705, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011029684997883409, + "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": 955.987, + "pct_cuda_time": 1.4306968076080822, + "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": 100.009, + "cuda_time_us": 131.806, + "pct_cuda_time": 0.1972562633420652, + "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": 131.806, + "pct_cuda_time": 0.1972562633420652, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.657, + "cuda_time_us": 440.572, + "pct_cuda_time": 0.6593446918436213, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0011029684997883409, + "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": 439.835, + "pct_cuda_time": 0.6582417233438329, + "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": 2454.849, + "cuda_time_us": 2066.3070000000002, + "pct_cuda_time": 3.092363001210512, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.756, + "cuda_time_us": 33.471, + "pct_cuda_time": 0.050091531419831144, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.050091531419831144, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1705.612, + "cuda_time_us": 474.71400000000006, + "pct_cuda_time": 0.7104404184647524, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.072, + "cuda_time_us": 210.173, + "pct_cuda_time": 0.3145375827761397, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 209.437, + "pct_cuda_time": 0.31343611084148, + "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": 467.533, + "cuda_time_us": 40.352, + "pct_cuda_time": 0.06038939606982242, + "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": 40.352, + "pct_cuda_time": 0.06038939606982242, + "trace": "_C::rotary_embedding(int64[3072], bfloat16[3072, 4096], bfloat16[3072, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 727.563, + "cuda_time_us": 72.767, + "pct_cuda_time": 0.10890055471383744, + "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": 16.127, + "pct_cuda_time": 0.0241351058291541, + "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": 55.232, + "pct_cuda_time": 0.08265828518359516, + "trace": "_vllm_fa3_C::fwd(bfloat16[3072, 32, 128], bfloat16[3072, 8, 128], bfloat16[3072, 8, 128], None, None, bfloat16[3072, 32, 128], int32[7], int32[7], None, None, None, 512, 512, 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": 0.002107163701088173, + "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[7], int32[7], None, None, None, 512, 512, 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": 213.551, + "cuda_time_us": 151.422, + "pct_cuda_time": 0.22661288490495268, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "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": 150.654, + "pct_cuda_time": 0.22546352288617733, + "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": 90.293, + "cuda_time_us": 30.303, + "pct_cuda_time": 0.04535041309238276, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.303, + "pct_cuda_time": 0.04535041309238276, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 513.623, + "cuda_time_us": 1527.8190000000002, + "pct_cuda_time": 2.2864806382335456, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 193.574, + "cuda_time_us": 956.051, + "pct_cuda_time": 1.4307925877763135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 955.315, + "pct_cuda_time": 1.4296911158416536, + "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": 101.457, + "cuda_time_us": 131.55, + "pct_cuda_time": 0.1968731426691401, + "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": 131.55, + "pct_cuda_time": 0.1968731426691401, + "trace": "_C::silu_and_mul(bfloat16[3072, 14336], bfloat16[3072, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 158.943, + "cuda_time_us": 440.218, + "pct_cuda_time": 0.658814907788092, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.001101471934659727, + "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": 439.482, + "pct_cuda_time": 0.6577134358534323, + "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": 74.818, + "cuda_time_us": 33.44, + "pct_cuda_time": 0.05004513790084411, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.05004513790084411, + "trace": "_C::fused_add_rms_norm(bfloat16[3072, 4096], bfloat16[3072, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 450.807, + "cuda_time_us": 364.79499999999996, + "pct_cuda_time": 0.5459394760926564, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 5.216, + "pct_cuda_time": 0.00780608371084937, + "trace": "index_select(bfloat16[3072, 4096], 0, int64[6])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[6, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 358.811, + "pct_cuda_time": 0.5369840303630317, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[6, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 3163.229, + "cuda_time_us": 124.861, + "pct_cuda_time": 0.18686261852384264, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.003735426561019944, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.0036875364769043037, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.0036396463927886634, + "trace": "copy_(int32[6], int32[6], True) <- _to_copy(int32[6], 3, 0, None, None, True, None) <- to(int32[6], 3, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.003783316645135584, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0011972521028910076, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0011493620187753672, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 4.383, + "pct_cuda_time": 0.006559444958714108, + "trace": "copy_(float32[6, 128256], bfloat16[6, 128256], False) <- _to_copy(bfloat16[6, 128256], 6, None, None, None, False, None) <- to(bfloat16[6, 128256], 6, False, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::elementwise_kernel<128, 4, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1})", + "cpu_time_us": 0, + "cuda_time_us": 5.6, + "pct_cuda_time": 0.008380764720237053, + "trace": "div_(float32[6, 128256], bfloat16[6, 1])" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::SoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 35.679, + "pct_cuda_time": 0.05339594722381033, + "trace": "_softmax(float32[6, 128256], -1, False) <- softmax(float32[6, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::LogSoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 28.383, + "pct_cuda_time": 0.04247700804544434, + "trace": "_log_softmax(float32[6, 128256], -1, False) <- log_softmax(float32[6, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 1.952, + "pct_cuda_time": 0.0029212951310540586, + "trace": "copy_(int64[6], int32[6], False) <- _to_copy(int32[6], 4, None, None, None, False, None) <- to(int32[6], 4, False, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::index_elementwise_kernel<128, 4, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1}>(long, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1})", + "cpu_time_us": 0, + "cuda_time_us": 6.176, + "pct_cuda_time": 0.00924278623431858, + "trace": "index(float32[6, 128256], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, long, 4> >(at::native::ReduceOp, unsigned int, long, 4>)", + "cpu_time_us": 0, + "cuda_time_us": 27.84, + "pct_cuda_time": 0.04166437318060707, + "trace": "argmax(float32[6, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 2.592, + "pct_cuda_time": 0.003879096813366865, + "trace": "copy_(int64[6], int64[6], False) <- _to_copy(int64[6], 4, 0, None, None, False, None) <- to(int64[6], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + }, + "decode_1": { + "metadata": { + "num_running_seqs": 6 + }, + "summary_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cuda_time_us": 6536.813999999998, + "pct_cuda_time": 93.33944095551921, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cuda_time_us": 5.824, + "pct_cuda_time": 0.08316113998730025, + "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": 5.824, + "pct_cuda_time": 0.08316113998730025, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cuda_time_us": 6527.949999999998, + "pct_cuda_time": 93.2128715281759, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cuda_time_us": 199.52100000000002, + "pct_cuda_time": 2.8489687176178116, + "invocations": 64 + }, + "children": [ + { + "entry": { + "name": "void vllm::rms_norm_kernel(c10::BFloat16*, c10::BFloat16 const*, c10::BFloat16 const*, float, int, int)", + "cuda_time_us": 4.16, + "pct_cuda_time": 0.05940081427664303, + "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": 195.36100000000002, + "pct_cuda_time": 2.7895679033411684, + "invocations": 63 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cuda_time_us": 2038.3760000000004, + "pct_cuda_time": 29.10605629854965, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cuda_time_us": 668.569, + "pct_cuda_time": 9.546524759644461, + "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": 668.569, + "pct_cuda_time": 9.546524759644461, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cuda_time_us": 122.592, + "pct_cuda_time": 1.7504963037986112, + "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": 122.592, + "pct_cuda_time": 1.7504963037986112, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cuda_time_us": 673.6260000000001, + "pct_cuda_time": 9.618733874499506, + "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": 85.95300000000002, + "pct_cuda_time": 1.2273264878654566, + "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": 545.752, + "pct_cuda_time": 7.792815671419828, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cuda_time_us": 41.92100000000001, + "pct_cuda_time": 0.5985917152142194, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cuda_time_us": 573.589, + "pct_cuda_time": 8.190301360607068, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cuda_time_us": 505.109, + "pct_cuda_time": 7.212472571745406, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cuda_time_us": 68.48000000000002, + "pct_cuda_time": 0.9778287888616625, + "invocations": 32 + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cuda_time_us": 4290.052999999999, + "pct_cuda_time": 61.25784651200846, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cuda_time_us": 2600.1889999999994, + "pct_cuda_time": 37.128207661819744, + "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": 2600.1889999999994, + "pct_cuda_time": 37.128207661819744, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cuda_time_us": 289.242, + "pct_cuda_time": 4.1300986353376885, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cuda_time_us": 289.242, + "pct_cuda_time": 4.1300986353376885, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cuda_time_us": 1400.622, + "pct_cuda_time": 19.999540214851038, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + 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long)", + "cuda_time_us": 4.96, + "pct_cuda_time": 0.07082404779138207, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cuda_time_us": 0.768, + "pct_cuda_time": 0.010966304174149483, + "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": 344.059, + "pct_cuda_time": 4.912832874809501, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cuda_time_us": 116.67, + "pct_cuda_time": 1.6659358177057555, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cuda_time_us": 5.343, + "pct_cuda_time": 0.0762929208365634, + "invocations": 7 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cuda_time_us": 4.353, + "pct_cuda_time": 0.06215666936207383, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::elementwise_kernel<128, 4, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1})", + "cuda_time_us": 5.536, + "pct_cuda_time": 0.07904877592199418, + "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": 34.784, + "pct_cuda_time": 0.49668219322085366, + "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": 28.255, + "pct_cuda_time": 0.4034543286986896, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cuda_time_us": 1.824, + "pct_cuda_time": 0.02604497241360502, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::index_elementwise_kernel<128, 4, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1}>(long, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1})", + "cuda_time_us": 6.24, + "pct_cuda_time": 0.08910122141496456, + "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": 27.712, + "pct_cuda_time": 0.3957008089505605, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cuda_time_us": 2.623, + "pct_cuda_time": 0.03745392688645065, + "invocations": 1 + }, + "children": [] + } + ] + } + ], + "model_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cpu_time_us": 78666.36, + "cuda_time_us": 6536.813999999998, + "pct_cuda_time": 93.33944095551921, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cpu_time_us": 294.012, + "cuda_time_us": 5.824, + "pct_cuda_time": 0.08316113998730025, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 5.824, + "pct_cuda_time": 0.08316113998730025, + "trace": "index_select(bfloat16[128256, 4096], 0, int64[6]) <- embedding(bfloat16[128256, 4096], int64[6], -1, False, False)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 4122.084, + "cuda_time_us": 210.783, + "pct_cuda_time": 3.00977928742155, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 273.58, + "cuda_time_us": 4.16, + "pct_cuda_time": 0.05940081427664303, + "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": 4.16, + "pct_cuda_time": 0.05940081427664303, + "trace": "_C::rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2997.244, + "cuda_time_us": 69.184, + "pct_cuda_time": 0.9878812343546326, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 528.929, + "cuda_time_us": 26.112, + "pct_cuda_time": 0.3728543419210824, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 26.112, + "pct_cuda_time": 0.3728543419210824, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 872.49, + "cuda_time_us": 3.681, + "pct_cuda_time": 0.052561153209693026, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.681, + "pct_cuda_time": 0.052561153209693026, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1042.747, + "cuda_time_us": 21.184, + "pct_cuda_time": 0.3024872234702899, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.657, + "pct_cuda_time": 0.037939414310827056, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.151, + "pct_cuda_time": 0.2448998475141117, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.376, + "pct_cuda_time": 0.019647961645351156, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 302.255, + "cuda_time_us": 18.207, + "pct_cuda_time": 0.25997851575356723, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.095, + "pct_cuda_time": 0.22982117927465612, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 122.177, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.044779075377777054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044779075377777054, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 613.257, + "cuda_time_us": 134.303, + "pct_cuda_time": 1.9177181634124973, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 233.106, + "cuda_time_us": 81.247, + "pct_cuda_time": 1.160129316715004, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.247, + "pct_cuda_time": 1.160129316715004, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 134.263, + "cuda_time_us": 8.832, + "pct_cuda_time": 0.12611249800271906, + "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": 0.12611249800271906, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 163.889, + "cuda_time_us": 44.224, + "pct_cuda_time": 0.6314763486947743, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6314763486947743, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2547.534, + "cuda_time_us": 204.89, + "pct_cuda_time": 2.9256328935436033, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.894, + "cuda_time_us": 3.135, + "pct_cuda_time": 0.04476479633588363, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04476479633588363, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1815.504, + "cuda_time_us": 64.125, + "pct_cuda_time": 0.9156435614158015, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 143.697, + "cuda_time_us": 21.376, + "pct_cuda_time": 0.3052287995138273, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.376, + "pct_cuda_time": 0.3052287995138273, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 565.2, + "cuda_time_us": 3.872, + "pct_cuda_time": 0.05528845021133698, + "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": 0.05528845021133698, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 765.646, + "cuda_time_us": 20.926000000000002, + "pct_cuda_time": 0.2988032306617866, + "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": 0.0379108562270402, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.959, + "pct_cuda_time": 0.2421582714705743, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.018734102964172033, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 176.804, + "cuda_time_us": 17.951, + "pct_cuda_time": 0.25632308102885076, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.839, + "pct_cuda_time": 0.22616574454993968, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.118, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04523600471836662, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04523600471836662, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 483.032, + "cuda_time_us": 134.462, + "pct_cuda_time": 1.9199885310735516, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 170.655, + "cuda_time_us": 80.831, + "pct_cuda_time": 1.1541892352873397, + "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": 80.831, + "pct_cuda_time": 1.1541892352873397, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 106.616, + "cuda_time_us": 9.023, + "pct_cuda_time": 0.128839795004363, + "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": 0.128839795004363, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.823, + "cuda_time_us": 44.608, + "pct_cuda_time": 0.6369595007818492, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.608, + "pct_cuda_time": 0.6369595007818492, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2393.025, + "cuda_time_us": 205.50200000000004, + "pct_cuda_time": 2.934371667182379, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.463, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04340828735600837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04340828735600837, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1697.578, + "cuda_time_us": 63.904, + "pct_cuda_time": 0.9124878931573549, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.471, + "cuda_time_us": 20.832, + "pct_cuda_time": 0.2974610007238047, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.832, + "pct_cuda_time": 0.2974610007238047, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 537.65, + "cuda_time_us": 3.968, + "pct_cuda_time": 0.056659238233105653, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.968, + "pct_cuda_time": 0.056659238233105653, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 704.928, + "cuda_time_us": 21.376, + "pct_cuda_time": 0.3052287995138273, + "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": 0.03792513526893363, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.408, + "pct_cuda_time": 0.24856956128072163, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.018734102964172033, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 163.319, + "cuda_time_us": 17.728, + "pct_cuda_time": 0.25313885468661723, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.22298151820770612, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.718, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04340828735600837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04340828735600837, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 458.375, + "cuda_time_us": 135.51800000000003, + "pct_cuda_time": 1.9350671993130077, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.554, + "cuda_time_us": 83.007, + "pct_cuda_time": 1.18526043044743, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.007, + "pct_cuda_time": 1.18526043044743, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.838, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.1279402153650773, + "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": 0.1279402153650773, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.104, + "cuda_time_us": 43.551, + "pct_cuda_time": 0.6218665535005002, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.551, + "pct_cuda_time": 0.6218665535005002, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2491.67, + "cuda_time_us": 204.34900000000002, + "pct_cuda_time": 2.9179079318792613, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.395, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04614986339954574, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04614986339954574, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1746.593, + "cuda_time_us": 62.784000000000006, + "pct_cuda_time": 0.8964953662367204, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.185, + "cuda_time_us": 20.512, + "pct_cuda_time": 0.29289170731790914, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.512, + "pct_cuda_time": 0.29289170731790914, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 521.72, + "cuda_time_us": 3.872, + "pct_cuda_time": 0.05528845021133698, + "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": 0.05528845021133698, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 738.27, + "cuda_time_us": 20.737000000000002, + "pct_cuda_time": 0.2961044917439295, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.657, + "pct_cuda_time": 0.037939414310827056, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.832, + "pct_cuda_time": 0.24034483315010952, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.01782024428299291, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 173.675, + "cuda_time_us": 17.663, + "pct_cuda_time": 0.2522107169635447, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.552, + "pct_cuda_time": 0.22206765952652702, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.111, + "pct_cuda_time": 0.03014305743701766, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.627, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04340828735600837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04340828735600837, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 512.772, + "cuda_time_us": 135.293, + "pct_cuda_time": 1.9318544148869872, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 200.147, + "cuda_time_us": 81.918, + "pct_cuda_time": 1.1697105538254915, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.918, + "pct_cuda_time": 1.1697105538254915, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.524, + "cuda_time_us": 9.216, + "pct_cuda_time": 0.1315956500897938, + "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": 0.1315956500897938, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 150.543, + "cuda_time_us": 44.159, + "pct_cuda_time": 0.6305482109717018, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.159, + "pct_cuda_time": 0.6305482109717018, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2332.148, + "cuda_time_us": 203.58300000000003, + "pct_cuda_time": 2.9069701857888988, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.103, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04340828735600837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04340828735600837, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1643.43, + "cuda_time_us": 63.486999999999995, + "pct_cuda_time": 0.9065335326877971, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 142.474, + "cuda_time_us": 20.767, + "pct_cuda_time": 0.29653286300073217, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.29653286300073217, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 484.227, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05483152087074741, + "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": 0.05483152087074741, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 708.248, + "cuda_time_us": 20.896, + "pct_cuda_time": 0.29837485940498387, + "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": 2.688, + "pct_cuda_time": 0.038382064609523196, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.928, + "pct_cuda_time": 0.2417156211718782, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01827717362358247, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 158.435, + "cuda_time_us": 17.983999999999998, + "pct_cuda_time": 0.2567942894113337, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.68, + "pct_cuda_time": 0.22389537688888528, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.304, + "pct_cuda_time": 0.03289891252244845, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.771, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.04479335441967048, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.04479335441967048, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 461.905, + "cuda_time_us": 133.919, + "pct_cuda_time": 1.912235011325423, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 171.029, + "cuda_time_us": 81.343, + "pct_cuda_time": 1.1615001047367728, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.343, + "pct_cuda_time": 1.1615001047367728, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.902, + "cuda_time_us": 8.961, + "pct_cuda_time": 0.12795449440697074, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.961, + "pct_cuda_time": 0.12795449440697074, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 139.632, + "cuda_time_us": 43.615, + "pct_cuda_time": 0.6227804121816793, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.615, + "pct_cuda_time": 0.6227804121816793, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2385.639, + "cuda_time_us": 202.527, + "pct_cuda_time": 2.8918915175494426, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.332, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.04479335441967048, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.04479335441967048, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1705.468, + "cuda_time_us": 63.199999999999996, + "pct_cuda_time": 0.9024354476643844, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 135.241, + "cuda_time_us": 20.32, + "pct_cuda_time": 0.2901501312743718, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.32, + "pct_cuda_time": 0.2901501312743718, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 483.512, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05483152087074741, + "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": 0.05483152087074741, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 764.451, + "cuda_time_us": 21.152, + "pct_cuda_time": 0.30203029412970034, + "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": 2.688, + "pct_cuda_time": 0.038382064609523196, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.152, + "pct_cuda_time": 0.2449141265560051, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.018734102964172033, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 165.547, + "cuda_time_us": 17.887999999999998, + "pct_cuda_time": 0.255423501389565, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.2234384475482957, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.24, + "pct_cuda_time": 0.031985053841269324, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.444, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.04479335441967048, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.04479335441967048, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 451.005, + "cuda_time_us": 133.053, + "pct_cuda_time": 1.8998693610457176, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.734, + "cuda_time_us": 81.215, + "pct_cuda_time": 1.1596723873744144, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.215, + "pct_cuda_time": 1.1596723873744144, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.158, + "cuda_time_us": 8.991, + "pct_cuda_time": 0.12838286566377344, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.991, + "pct_cuda_time": 0.12838286566377344, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.726, + "cuda_time_us": 42.847, + "pct_cuda_time": 0.6118141080075299, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 42.847, + "pct_cuda_time": 0.6118141080075299, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2332.84, + "cuda_time_us": 205.022, + "pct_cuda_time": 2.9275177270735355, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.936, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04386521669659793, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04386521669659793, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1635.344, + "cuda_time_us": 63.648, + "pct_cuda_time": 0.9088324584326384, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 134.995, + "cuda_time_us": 20.448, + "pct_cuda_time": 0.29197784863673, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.448, + "pct_cuda_time": 0.29197784863673, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 470.935, + "cuda_time_us": 3.872, + "pct_cuda_time": 0.05528845021133698, + "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": 0.05528845021133698, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 707.047, + "cuda_time_us": 20.928, + "pct_cuda_time": 0.2988317887455734, + "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": 2.688, + "pct_cuda_time": 0.038382064609523196, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.928, + "pct_cuda_time": 0.2417156211718782, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.018734102964172033, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 167.613, + "cuda_time_us": 18.4, + "pct_cuda_time": 0.26273437083899803, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.288, + "pct_cuda_time": 0.23257703436008695, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.316, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.044779075377777054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044779075377777054, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.191, + "cuda_time_us": 135.166, + "pct_cuda_time": 1.930040976566522, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 171.716, + "cuda_time_us": 82.751, + "pct_cuda_time": 1.1816049957227135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 82.751, + "pct_cuda_time": 1.1816049957227135, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.03, + "cuda_time_us": 9.055, + "pct_cuda_time": 0.12929672434495257, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.055, + "pct_cuda_time": 0.12929672434495257, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.616, + "cuda_time_us": 43.36, + "pct_cuda_time": 0.6191392564988563, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.36, + "pct_cuda_time": 0.6191392564988563, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2370.605, + "cuda_time_us": 203.83800000000002, + "pct_cuda_time": 2.9106113414717223, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.026, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04340828735600837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04340828735600837, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1692.188, + "cuda_time_us": 63.52, + "pct_cuda_time": 0.9070047410702801, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 141.531, + "cuda_time_us": 20.736, + "pct_cuda_time": 0.29609021270203606, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.29609021270203606, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 537.32, + "cuda_time_us": 4.0, + "pct_cuda_time": 0.057116167573695226, + "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": 4.0, + "pct_cuda_time": 0.057116167573695226, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 696.486, + "cuda_time_us": 21.152, + "pct_cuda_time": 0.30203029412970034, + "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": 0.0379108562270402, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.184, + "pct_cuda_time": 0.2453710558965947, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.313, + "pct_cuda_time": 0.018748382006065455, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 158.204, + "cuda_time_us": 17.631999999999998, + "pct_cuda_time": 0.2517680666648485, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.552, + "pct_cuda_time": 0.22206765952652702, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.08, + "pct_cuda_time": 0.029700407138321516, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.909, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.045692934058956185, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.045692934058956185, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 457.088, + "cuda_time_us": 134.078, + "pct_cuda_time": 1.914505378986477, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.026, + "cuda_time_us": 81.375, + "pct_cuda_time": 1.1619570340773622, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.375, + "pct_cuda_time": 1.1619570340773622, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.637, + "cuda_time_us": 8.927, + "pct_cuda_time": 0.1274690069825943, + "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": 0.1274690069825943, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.066, + "cuda_time_us": 43.776, + "pct_cuda_time": 0.6250793379265205, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.776, + "pct_cuda_time": 0.6250793379265205, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2256.027, + "cuda_time_us": 203.036, + "pct_cuda_time": 2.899159549873196, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.09, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04386521669659793, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04386521669659793, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1563.32, + "cuda_time_us": 63.358999999999995, + "pct_cuda_time": 0.9047058153254388, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 135.176, + "cuda_time_us": 20.672, + "pct_cuda_time": 0.29517635402085696, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.29517635402085696, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 469.469, + "cuda_time_us": 3.904, + "pct_cuda_time": 0.05574537955192653, + "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": 3.904, + "pct_cuda_time": 0.05574537955192653, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 667.645, + "cuda_time_us": 20.864, + "pct_cuda_time": 0.2979179300643943, + "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": 0.03792513526893363, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.928, + "pct_cuda_time": 0.2417156211718782, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01827717362358247, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 153.871, + "cuda_time_us": 17.919, + "pct_cuda_time": 0.25586615168826116, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.807, + "pct_cuda_time": 0.2257088152093501, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.631, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04295135801541881, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04295135801541881, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 476.674, + "cuda_time_us": 133.597, + "pct_cuda_time": 1.9076371598357404, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.577, + "cuda_time_us": 80.318, + "pct_cuda_time": 1.1468640867960131, + "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": 80.318, + "pct_cuda_time": 1.1468640867960131, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.914, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.12839714470566688, + "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": 0.12839714470566688, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 145.89, + "cuda_time_us": 44.287, + "pct_cuda_time": 0.6323759283340601, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.287, + "pct_cuda_time": 0.6323759283340601, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2373.573, + "cuda_time_us": 202.94299999999998, + "pct_cuda_time": 2.897831598977107, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.134, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04386521669659793, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04386521669659793, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1704.184, + "cuda_time_us": 63.072, + "pct_cuda_time": 0.9006077303020263, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.524, + "cuda_time_us": 20.416, + "pct_cuda_time": 0.29152091929614043, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.416, + "pct_cuda_time": 0.29152091929614043, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 517.009, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05483152087074741, + "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": 0.05483152087074741, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 724.897, + "cuda_time_us": 20.96, + "pct_cuda_time": 0.299288718086163, + "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": 0.03792513526893363, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.024, + "pct_cuda_time": 0.24308640919364688, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01827717362358247, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 156.207, + "cuda_time_us": 17.856, + "pct_cuda_time": 0.2549665720489755, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.744, + "pct_cuda_time": 0.2248092355700644, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.634, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04295135801541881, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04295135801541881, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 446.975, + "cuda_time_us": 133.791, + "pct_cuda_time": 1.9104072939630643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 156.403, + "cuda_time_us": 81.407, + "pct_cuda_time": 1.1624139634179516, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.407, + "pct_cuda_time": 1.1624139634179516, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.053, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.1288540740462564, + "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": 0.1288540740462564, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.774, + "cuda_time_us": 43.36, + "pct_cuda_time": 0.6191392564988563, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.36, + "pct_cuda_time": 0.6191392564988563, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2281.984, + "cuda_time_us": 202.81400000000002, + "pct_cuda_time": 2.895989602572856, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.566, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04340828735600837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04340828735600837, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1633.85, + "cuda_time_us": 63.486999999999995, + "pct_cuda_time": 0.9065335326877971, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.409, + "cuda_time_us": 20.608, + "pct_cuda_time": 0.2942624953396778, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.2942624953396778, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 476.294, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05483152087074741, + "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": 0.05483152087074741, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 713.031, + "cuda_time_us": 20.959, + "pct_cuda_time": 0.29927443904426954, + "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": 2.688, + "pct_cuda_time": 0.038382064609523196, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.991, + "pct_cuda_time": 0.2426152008111639, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01827717362358247, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 154.451, + "cuda_time_us": 18.080000000000002, + "pct_cuda_time": 0.2581650774331024, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.775, + "pct_cuda_time": 0.22525188586876055, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.305, + "pct_cuda_time": 0.03291319156434187, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.533, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04340828735600837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04340828735600837, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 441.257, + "cuda_time_us": 133.247, + "pct_cuda_time": 1.9026394951730423, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 153.128, + "cuda_time_us": 80.959, + "pct_cuda_time": 1.156016952649698, + "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": 80.959, + "pct_cuda_time": 1.156016952649698, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.228, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.12748328602448775, + "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": 0.12748328602448775, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 139.217, + "cuda_time_us": 43.36, + "pct_cuda_time": 0.6191392564988563, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.36, + "pct_cuda_time": 0.6191392564988563, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2404.298, + "cuda_time_us": 203.005, + "pct_cuda_time": 2.8987168995744996, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.195, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.044779075377777054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044779075377777054, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1697.956, + "cuda_time_us": 62.944, + "pct_cuda_time": 0.8987800129396681, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 132.735, + "cuda_time_us": 20.448, + "pct_cuda_time": 0.29197784863673, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.448, + "pct_cuda_time": 0.29197784863673, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 500.094, + "cuda_time_us": 3.777, + "pct_cuda_time": 0.053931941231461715, + "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": 3.777, + "pct_cuda_time": 0.053931941231461715, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 709.356, + "cuda_time_us": 20.8, + "pct_cuda_time": 0.29700407138321516, + "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": 0.03792513526893363, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.832, + "pct_cuda_time": 0.24034483315010952, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.018734102964172033, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 167.087, + "cuda_time_us": 17.919, + "pct_cuda_time": 0.25586615168826116, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.807, + "pct_cuda_time": 0.2257088152093501, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.139, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.044322146037187496, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044322146037187496, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 480.975, + "cuda_time_us": 133.821, + "pct_cuda_time": 1.910835665219867, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 170.334, + "cuda_time_us": 80.863, + "pct_cuda_time": 1.1546461646279291, + "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": 80.863, + "pct_cuda_time": 1.1546461646279291, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.554, + "cuda_time_us": 9.055, + "pct_cuda_time": 0.12929672434495257, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.055, + "pct_cuda_time": 0.12929672434495257, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 147.439, + "cuda_time_us": 43.903, + "pct_cuda_time": 0.6268927762469854, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6268927762469854, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2382.333, + "cuda_time_us": 205.245, + "pct_cuda_time": 2.930701953415769, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.022, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04340828735600837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04340828735600837, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1695.168, + "cuda_time_us": 64.287, + "pct_cuda_time": 0.9179567662025363, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.911, + "cuda_time_us": 21.119, + "pct_cuda_time": 0.30155908574721735, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.119, + "pct_cuda_time": 0.30155908574721735, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 490.919, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.05391766218956829, + "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": 0.05391766218956829, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 750.773, + "cuda_time_us": 20.992, + "pct_cuda_time": 0.2997456474267525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.72, + "pct_cuda_time": 0.038838993950112755, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.992, + "pct_cuda_time": 0.2426294798530573, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01827717362358247, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 165.246, + "cuda_time_us": 18.4, + "pct_cuda_time": 0.26273437083899803, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.288, + "pct_cuda_time": 0.23257703436008695, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.737, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04340828735600837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04340828735600837, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 464.405, + "cuda_time_us": 134.878, + "pct_cuda_time": 1.9259286125012158, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 171.682, + "cuda_time_us": 81.118, + "pct_cuda_time": 1.1582873203107522, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.118, + "pct_cuda_time": 1.1582873203107522, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.15, + "cuda_time_us": 9.344, + "pct_cuda_time": 0.13342336745215203, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.344, + "pct_cuda_time": 0.13342336745215203, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.694, + "cuda_time_us": 44.416, + "pct_cuda_time": 0.6342179247383117, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.416, + "pct_cuda_time": 0.6342179247383117, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2260.983, + "cuda_time_us": 203.09699999999998, + "pct_cuda_time": 2.9000305714286942, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.358, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.044322146037187496, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044322146037187496, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1568.665, + "cuda_time_us": 63.164, + "pct_cuda_time": 0.9019214021562213, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 134.874, + "cuda_time_us": 20.479, + "pct_cuda_time": 0.2924204989354261, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.479, + "pct_cuda_time": 0.2924204989354261, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 465.252, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05483152087074741, + "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": 0.05483152087074741, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 672.553, + "cuda_time_us": 21.151, + "pct_cuda_time": 0.3020160150878069, + "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": 2.752, + "pct_cuda_time": 0.03929592329070231, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.991, + "pct_cuda_time": 0.2426152008111639, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.408, + "pct_cuda_time": 0.020104890985940718, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 155.101, + "cuda_time_us": 17.694, + "pct_cuda_time": 0.2526533672622408, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.583, + "pct_cuda_time": 0.22251030982522318, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.111, + "pct_cuda_time": 0.03014305743701766, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.409, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04340828735600837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04340828735600837, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.803, + "cuda_time_us": 133.789, + "pct_cuda_time": 1.9103787358792774, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.38, + "cuda_time_us": 80.606, + "pct_cuda_time": 1.1509764508613192, + "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": 80.606, + "pct_cuda_time": 1.1509764508613192, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 116.717, + "cuda_time_us": 9.152, + "pct_cuda_time": 0.13068179140861466, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.152, + "pct_cuda_time": 0.13068179140861466, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.913, + "cuda_time_us": 44.031, + "pct_cuda_time": 0.6287204936093436, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.031, + "pct_cuda_time": 0.6287204936093436, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2380.757, + "cuda_time_us": 203.26100000000002, + "pct_cuda_time": 2.9023723342992165, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.1, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.044779075377777054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044779075377777054, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1703.97, + "cuda_time_us": 63.359, + "pct_cuda_time": 0.9047058153254388, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.054, + "cuda_time_us": 20.544, + "pct_cuda_time": 0.29334863665849864, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.544, + "pct_cuda_time": 0.29334863665849864, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 532.244, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.05346073284897873, + "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": 0.05346073284897873, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 714.837, + "cuda_time_us": 21.12, + "pct_cuda_time": 0.3015733647891108, + "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": 0.03792513526893363, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.184, + "pct_cuda_time": 0.2453710558965947, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01827717362358247, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 155.068, + "cuda_time_us": 17.951, + "pct_cuda_time": 0.25632308102885076, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.839, + "pct_cuda_time": 0.22616574454993968, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.866, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04523600471836662, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04523600471836662, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 455.597, + "cuda_time_us": 133.598, + "pct_cuda_time": 1.9076514388776338, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 167.086, + "cuda_time_us": 81.215, + "pct_cuda_time": 1.1596723873744144, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.215, + "pct_cuda_time": 1.1596723873744144, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.151, + "cuda_time_us": 9.216, + "pct_cuda_time": 0.1315956500897938, + "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": 0.1315956500897938, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.146, + "cuda_time_us": 43.167, + "pct_cuda_time": 0.6163834014134254, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6163834014134254, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2296.416, + "cuda_time_us": 203.935, + "pct_cuda_time": 2.911996408535384, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.402, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.044779075377777054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044779075377777054, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1626.502, + "cuda_time_us": 63.264, + "pct_cuda_time": 0.9033493063455637, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.485, + "cuda_time_us": 20.736, + "pct_cuda_time": 0.29609021270203606, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.29609021270203606, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 487.174, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05437459153015785, + "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": 0.05437459153015785, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 710.773, + "cuda_time_us": 20.897000000000002, + "pct_cuda_time": 0.2983891384468773, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.657, + "pct_cuda_time": 0.037939414310827056, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.96, + "pct_cuda_time": 0.24217255051246778, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01827717362358247, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 153.042, + "cuda_time_us": 17.823, + "pct_cuda_time": 0.2544953636664925, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.712, + "pct_cuda_time": 0.22435230622947483, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.111, + "pct_cuda_time": 0.03014305743701766, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.683, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04340828735600837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04340828735600837, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 453.715, + "cuda_time_us": 134.495, + "pct_cuda_time": 1.9204597394560348, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.052, + "cuda_time_us": 81.951, + "pct_cuda_time": 1.1701817622079742, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.951, + "pct_cuda_time": 1.1701817622079742, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.259, + "cuda_time_us": 9.152, + "pct_cuda_time": 0.13068179140861466, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.152, + "pct_cuda_time": 0.13068179140861466, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.58, + "cuda_time_us": 43.392, + "pct_cuda_time": 0.6195961858394459, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6195961858394459, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2335.812, + "cuda_time_us": 204.668, + "pct_cuda_time": 2.9224629462432636, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.773, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.044779075377777054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044779075377777054, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1640.934, + "cuda_time_us": 64.83, + "pct_cuda_time": 0.9257102859506653, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 132.438, + "cuda_time_us": 20.8, + "pct_cuda_time": 0.29700407138321516, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.8, + "pct_cuda_time": 0.29700407138321516, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 484.665, + "cuda_time_us": 3.775, + "pct_cuda_time": 0.053903383147674865, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.775, + "pct_cuda_time": 0.053903383147674865, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 705.525, + "cuda_time_us": 21.695, + "pct_cuda_time": 0.3097838138778295, + "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": 2.848, + "pct_cuda_time": 0.040666711312470995, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.343, + "pct_cuda_time": 0.24764142355764907, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.021475679007709404, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 162.276, + "cuda_time_us": 18.56, + "pct_cuda_time": 0.26501901754194584, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.447, + "pct_cuda_time": 0.23484740202114135, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.113, + "pct_cuda_time": 0.030171615520804503, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.065, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.044322146037187496, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044322146037187496, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 465.105, + "cuda_time_us": 133.598, + "pct_cuda_time": 1.9076514388776338, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 166.693, + "cuda_time_us": 81.599, + "pct_cuda_time": 1.1651555394614892, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.599, + "pct_cuda_time": 1.1651555394614892, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.944, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.12748328602448775, + "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": 0.12748328602448775, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 143.185, + "cuda_time_us": 43.071, + "pct_cuda_time": 0.6150126133916567, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.071, + "pct_cuda_time": 0.6150126133916567, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2316.855, + "cuda_time_us": 202.71800000000002, + "pct_cuda_time": 2.8946188145510874, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.18, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04295135801541881, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04295135801541881, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1647.674, + "cuda_time_us": 62.815, + "pct_cuda_time": 0.8969380165354164, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 138.016, + "cuda_time_us": 20.32, + "pct_cuda_time": 0.2901501312743718, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.32, + "pct_cuda_time": 0.2901501312743718, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 520.86, + "cuda_time_us": 3.775, + "pct_cuda_time": 0.053903383147674865, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.775, + "pct_cuda_time": 0.053903383147674865, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 684.252, + "cuda_time_us": 21.024, + "pct_cuda_time": 0.30020257676734213, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.624, + "pct_cuda_time": 0.037468205928344066, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.12, + "pct_cuda_time": 0.2444571972154156, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01827717362358247, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 157.256, + "cuda_time_us": 17.695999999999998, + "pct_cuda_time": 0.2526819253460276, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.552, + "pct_cuda_time": 0.22206765952652702, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.144, + "pct_cuda_time": 0.03061426581950064, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.768, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04523600471836662, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04523600471836662, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 448.633, + "cuda_time_us": 133.727, + "pct_cuda_time": 1.9094934352818855, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 156.086, + "cuda_time_us": 80.607, + "pct_cuda_time": 1.1509907299032127, + "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": 80.607, + "pct_cuda_time": 1.1509907299032127, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.377, + "cuda_time_us": 8.929, + "pct_cuda_time": 0.12749756506638116, + "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": 8.929, + "pct_cuda_time": 0.12749756506638116, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.682, + "cuda_time_us": 44.191, + "pct_cuda_time": 0.6310051403122914, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6310051403122914, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2363.964, + "cuda_time_us": 204.06, + "pct_cuda_time": 2.913781288772062, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.219, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.044322146037187496, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044322146037187496, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1648.609, + "cuda_time_us": 63.36, + "pct_cuda_time": 0.9047200943673324, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 135.947, + "cuda_time_us": 20.768, + "pct_cuda_time": 0.2965471420426256, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.2965471420426256, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 494.124, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.05391766218956829, + "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": 0.05391766218956829, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 699.22, + "cuda_time_us": 20.896, + "pct_cuda_time": 0.29837485940498387, + "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": 2.688, + "pct_cuda_time": 0.038382064609523196, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.928, + "pct_cuda_time": 0.2417156211718782, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01827717362358247, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 165.099, + "cuda_time_us": 17.92, + "pct_cuda_time": 0.2558804307301546, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.808, + "pct_cuda_time": 0.2257230942512435, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.792, + "cuda_time_us": 3.039, + "pct_cuda_time": 0.04339400831411495, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.039, + "pct_cuda_time": 0.04339400831411495, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 489.615, + "cuda_time_us": 134.55700000000002, + "pct_cuda_time": 1.9213450400534273, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.299, + "cuda_time_us": 81.119, + "pct_cuda_time": 1.1583015993526458, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.119, + "pct_cuda_time": 1.1583015993526458, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.081, + "cuda_time_us": 9.055, + "pct_cuda_time": 0.12929672434495257, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.055, + "pct_cuda_time": 0.12929672434495257, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 168.463, + "cuda_time_us": 44.383, + "pct_cuda_time": 0.6337467163558288, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.383, + "pct_cuda_time": 0.6337467163558288, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2322.129, + "cuda_time_us": 203.26099999999997, + "pct_cuda_time": 2.9023723342992156, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.422, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04340828735600837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04340828735600837, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1609.134, + "cuda_time_us": 63.903, + "pct_cuda_time": 0.9124736141154614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 140.257, + "cuda_time_us": 21.343, + "pct_cuda_time": 0.3047575911313443, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.343, + "pct_cuda_time": 0.3047575911313443, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 477.451, + "cuda_time_us": 3.745, + "pct_cuda_time": 0.05347501189087216, + "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": 3.745, + "pct_cuda_time": 0.05347501189087216, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 690.327, + "cuda_time_us": 21.055, + "pct_cuda_time": 0.30064522706603825, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.624, + "pct_cuda_time": 0.037468205928344066, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.151, + "pct_cuda_time": 0.2448998475141117, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01827717362358247, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 154.665, + "cuda_time_us": 17.759999999999998, + "pct_cuda_time": 0.2535957840272068, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.2234384475482957, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.594, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.04479335441967048, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.04479335441967048, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 491.75, + "cuda_time_us": 133.18099999999998, + "pct_cuda_time": 1.9016970784080756, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 156.735, + "cuda_time_us": 80.895, + "pct_cuda_time": 1.1551030939685187, + "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": 80.895, + "pct_cuda_time": 1.1551030939685187, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.499, + "cuda_time_us": 9.023, + "pct_cuda_time": 0.128839795004363, + "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": 0.128839795004363, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 187.186, + "cuda_time_us": 43.263, + "pct_cuda_time": 0.617754189435194, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.263, + "pct_cuda_time": 0.617754189435194, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2336.815, + "cuda_time_us": 204.637, + "pct_cuda_time": 2.9220202959445674, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.913, + "cuda_time_us": 3.264, + "pct_cuda_time": 0.0466067927401353, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.0466067927401353, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1648.323, + "cuda_time_us": 64.38300000000001, + "pct_cuda_time": 0.919327554224305, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 135.341, + "cuda_time_us": 20.671, + "pct_cuda_time": 0.29516207497896346, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.29516207497896346, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 472.574, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.05391766218956829, + "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": 0.05391766218956829, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 674.963, + "cuda_time_us": 21.376, + "pct_cuda_time": 0.3052287995138273, + "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": 2.688, + "pct_cuda_time": 0.038382064609523196, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.28, + "pct_cuda_time": 0.24674184391836337, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.408, + "pct_cuda_time": 0.020104890985940718, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 168.187, + "cuda_time_us": 18.560000000000002, + "pct_cuda_time": 0.26501901754194584, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.448, + "pct_cuda_time": 0.23486168106303476, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.673, + "cuda_time_us": 3.231, + "pct_cuda_time": 0.04613558435765231, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.231, + "pct_cuda_time": 0.04613558435765231, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 463.945, + "cuda_time_us": 133.759, + "pct_cuda_time": 1.9099503646224747, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.421, + "cuda_time_us": 80.511, + "pct_cuda_time": 1.149619941881444, + "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": 80.511, + "pct_cuda_time": 1.149619941881444, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.713, + "cuda_time_us": 9.088, + "pct_cuda_time": 0.12976793272743553, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.088, + "pct_cuda_time": 0.12976793272743553, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.871, + "cuda_time_us": 44.16, + "pct_cuda_time": 0.6305624900135952, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6305624900135952, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2374.549, + "cuda_time_us": 203.22700000000003, + "pct_cuda_time": 2.90188684687484, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.218, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04340828735600837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04340828735600837, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1700.364, + "cuda_time_us": 63.004999999999995, + "pct_cuda_time": 0.8996510344951669, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.092, + "cuda_time_us": 20.608, + "pct_cuda_time": 0.2942624953396778, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.2942624953396778, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 513.834, + "cuda_time_us": 3.775, + "pct_cuda_time": 0.053903383147674865, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.775, + "pct_cuda_time": 0.053903383147674865, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 723.008, + "cuda_time_us": 20.959, + "pct_cuda_time": 0.29927443904426954, + "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": 0.03792513526893363, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.992, + "pct_cuda_time": 0.2426294798530573, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.311, + "pct_cuda_time": 0.018719823922278608, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 157.207, + "cuda_time_us": 17.663, + "pct_cuda_time": 0.2522107169635447, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.551, + "pct_cuda_time": 0.2220533804846336, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.571, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.044779075377777054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044779075377777054, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 454.758, + "cuda_time_us": 134.04600000000002, + "pct_cuda_time": 1.9140484496458876, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 156.563, + "cuda_time_us": 81.471, + "pct_cuda_time": 1.163327822099131, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.471, + "pct_cuda_time": 1.163327822099131, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.689, + "cuda_time_us": 9.055, + "pct_cuda_time": 0.12929672434495257, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.055, + "pct_cuda_time": 0.12929672434495257, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.978, + "cuda_time_us": 43.52, + "pct_cuda_time": 0.6214239032018041, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.52, + "pct_cuda_time": 0.6214239032018041, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2349.205, + "cuda_time_us": 204.031, + "pct_cuda_time": 2.9133671965571524, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.786, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04295135801541881, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04295135801541881, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1656.75, + "cuda_time_us": 63.488, + "pct_cuda_time": 0.9065478117296905, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.054, + "cuda_time_us": 20.448, + "pct_cuda_time": 0.29197784863673, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.448, + "pct_cuda_time": 0.29197784863673, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 505.097, + "cuda_time_us": 3.904, + "pct_cuda_time": 0.05574537955192653, + "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": 3.904, + "pct_cuda_time": 0.05574537955192653, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 696.362, + "cuda_time_us": 21.12, + "pct_cuda_time": 0.3015733647891108, + "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": 2.784, + "pct_cuda_time": 0.03975285263129187, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.056, + "pct_cuda_time": 0.24354333853423646, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01827717362358247, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 164.779, + "cuda_time_us": 18.016, + "pct_cuda_time": 0.25725121875192325, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.84, + "pct_cuda_time": 0.2261800235918331, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.176, + "pct_cuda_time": 0.031071195160090204, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.562, + "cuda_time_us": 3.296, + "pct_cuda_time": 0.04706372208072486, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.296, + "pct_cuda_time": 0.04706372208072486, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 469.612, + "cuda_time_us": 134.239, + "pct_cuda_time": 1.9168043047313184, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 171.955, + "cuda_time_us": 80.479, + "pct_cuda_time": 1.1491630125408543, + "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": 80.479, + "pct_cuda_time": 1.1491630125408543, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.06, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.1311387207492042, + "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": 0.1311387207492042, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.202, + "cuda_time_us": 44.576, + "pct_cuda_time": 0.6365025714412595, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.576, + "pct_cuda_time": 0.6365025714412595, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2374.726, + "cuda_time_us": 204.12600000000003, + "pct_cuda_time": 2.9147237055370283, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.171, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04295135801541881, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04295135801541881, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1682.038, + "cuda_time_us": 63.456, + "pct_cuda_time": 0.9060908823891011, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.166, + "cuda_time_us": 20.576, + "pct_cuda_time": 0.29380556599908825, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.576, + "pct_cuda_time": 0.29380556599908825, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 534.151, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05437459153015785, + "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": 0.05437459153015785, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 702.719, + "cuda_time_us": 21.216, + "pct_cuda_time": 0.3029441528108795, + "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": 2.784, + "pct_cuda_time": 0.03975285263129187, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.152, + "pct_cuda_time": 0.2449141265560051, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01827717362358247, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 157.491, + "cuda_time_us": 17.856, + "pct_cuda_time": 0.2549665720489755, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.583, + "pct_cuda_time": 0.22251030982522318, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.273, + "pct_cuda_time": 0.032456262223752315, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.25, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04340828735600837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04340828735600837, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 470.153, + "cuda_time_us": 134.622, + "pct_cuda_time": 1.9222731777764999, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 154.376, + "cuda_time_us": 81.631, + "pct_cuda_time": 1.1656124688020786, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.631, + "pct_cuda_time": 1.1656124688020786, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.55, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.1288540740462564, + "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": 0.1288540740462564, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 165.546, + "cuda_time_us": 43.967, + "pct_cuda_time": 0.6278066349281644, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.967, + "pct_cuda_time": 0.6278066349281644, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2239.563, + "cuda_time_us": 203.485, + "pct_cuda_time": 2.9055708396833433, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.81, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.044779075377777054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044779075377777054, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1601.86, + "cuda_time_us": 63.902, + "pct_cuda_time": 0.912459335073568, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.247, + "cuda_time_us": 21.087, + "pct_cuda_time": 0.3011021564066278, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.087, + "pct_cuda_time": 0.3011021564066278, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 494.012, + "cuda_time_us": 4.0, + "pct_cuda_time": 0.057116167573695226, + "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": 4.0, + "pct_cuda_time": 0.057116167573695226, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 683.413, + "cuda_time_us": 21.087, + "pct_cuda_time": 0.3011021564066278, + "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": 2.688, + "pct_cuda_time": 0.038382064609523196, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.119, + "pct_cuda_time": 0.24444291817352212, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01827717362358247, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 149.172, + "cuda_time_us": 17.728, + "pct_cuda_time": 0.25313885468661723, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.22298151820770612, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.587, + "cuda_time_us": 3.041, + "pct_cuda_time": 0.04342256639790179, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04342256639790179, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 432.84, + "cuda_time_us": 133.406, + "pct_cuda_time": 1.9049098628340964, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 150.392, + "cuda_time_us": 80.415, + "pct_cuda_time": 1.1482491538596755, + "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": 80.415, + "pct_cuda_time": 1.1482491538596755, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.476, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.1279402153650773, + "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": 0.1279402153650773, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.36, + "cuda_time_us": 44.031, + "pct_cuda_time": 0.6287204936093436, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.031, + "pct_cuda_time": 0.6287204936093436, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2350.278, + "cuda_time_us": 204.736, + "pct_cuda_time": 2.923433921092016, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.049, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04295135801541881, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04295135801541881, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1673.626, + "cuda_time_us": 64.38499999999999, + "pct_cuda_time": 0.9193561123080917, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.541, + "cuda_time_us": 21.408, + "pct_cuda_time": 0.30568572885441686, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.30568572885441686, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 503.974, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05437459153015785, + "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": 0.05437459153015785, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 714.475, + "cuda_time_us": 21.281, + "pct_cuda_time": 0.30387229053395204, + "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": 2.848, + "pct_cuda_time": 0.040666711312470995, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.12, + "pct_cuda_time": 0.2444571972154156, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.313, + "pct_cuda_time": 0.018748382006065455, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 159.748, + "cuda_time_us": 17.887999999999998, + "pct_cuda_time": 0.255423501389565, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.776, + "pct_cuda_time": 0.225266164910654, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.004, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.044779075377777054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044779075377777054, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 457.303, + "cuda_time_us": 134.207, + "pct_cuda_time": 1.9163473753907287, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 155.61, + "cuda_time_us": 81.695, + "pct_cuda_time": 1.1665263274832578, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.695, + "pct_cuda_time": 1.1665263274832578, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.148, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.12839714470566688, + "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": 0.12839714470566688, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.468, + "cuda_time_us": 43.52, + "pct_cuda_time": 0.6214239032018041, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.52, + "pct_cuda_time": 0.6214239032018041, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2258.931, + "cuda_time_us": 203.48600000000002, + "pct_cuda_time": 2.9055851187252366, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.436, + "cuda_time_us": 2.975, + "pct_cuda_time": 0.042480149632935824, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.042480149632935824, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1603.191, + "cuda_time_us": 63.168, + "pct_cuda_time": 0.9019785183237949, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.172, + "cuda_time_us": 20.48, + "pct_cuda_time": 0.29243477797731954, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.48, + "pct_cuda_time": 0.29243477797731954, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 477.835, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.05391766218956829, + "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": 0.05391766218956829, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 684.321, + "cuda_time_us": 20.96, + "pct_cuda_time": 0.299288718086163, + "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": 2.688, + "pct_cuda_time": 0.038382064609523196, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.928, + "pct_cuda_time": 0.2417156211718782, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.344, + "pct_cuda_time": 0.019191032304761598, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 157.127, + "cuda_time_us": 17.951999999999998, + "pct_cuda_time": 0.25633736007074415, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.2234384475482957, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.304, + "pct_cuda_time": 0.03289891252244845, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.986, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04386521669659793, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04386521669659793, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 444.054, + "cuda_time_us": 134.27100000000002, + "pct_cuda_time": 1.917261234071908, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 153.754, + "cuda_time_us": 82.208, + "pct_cuda_time": 1.1738514759745844, + "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": 82.208, + "pct_cuda_time": 1.1738514759745844, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.193, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.12839714470566688, + "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": 0.12839714470566688, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 145.353, + "cuda_time_us": 43.071, + "pct_cuda_time": 0.6150126133916567, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.071, + "pct_cuda_time": 0.6150126133916567, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2369.152, + "cuda_time_us": 203.00700000000003, + "pct_cuda_time": 2.898745457658287, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.435, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.045692934058956185, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.045692934058956185, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1678.838, + "cuda_time_us": 63.072, + "pct_cuda_time": 0.9006077303020263, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.902, + "cuda_time_us": 20.448, + "pct_cuda_time": 0.29197784863673, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.448, + "pct_cuda_time": 0.29197784863673, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 464.243, + "cuda_time_us": 4.032, + "pct_cuda_time": 0.05757309691428479, + "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": 4.032, + "pct_cuda_time": 0.05757309691428479, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 763.195, + "cuda_time_us": 20.928, + "pct_cuda_time": 0.2988317887455734, + "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": 0.03792513526893363, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.992, + "pct_cuda_time": 0.2426294798530573, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01827717362358247, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 166.739, + "cuda_time_us": 17.664, + "pct_cuda_time": 0.25222499600543813, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.552, + "pct_cuda_time": 0.22206765952652702, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.228, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.045692934058956185, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.045692934058956185, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 472.086, + "cuda_time_us": 133.53500000000003, + "pct_cuda_time": 1.9067518592383481, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 175.715, + "cuda_time_us": 81.343, + "pct_cuda_time": 1.1615001047367728, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.343, + "pct_cuda_time": 1.1615001047367728, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.081, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.12839714470566688, + "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": 0.12839714470566688, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.009, + "cuda_time_us": 43.2, + "pct_cuda_time": 0.6168546097959084, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.2, + "pct_cuda_time": 0.6168546097959084, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2296.699, + "cuda_time_us": 203.709, + "pct_cuda_time": 2.90876934506747, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.762, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04386521669659793, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04386521669659793, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1644.743, + "cuda_time_us": 63.775, + "pct_cuda_time": 0.9106458967531033, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.538, + "cuda_time_us": 21.056, + "pct_cuda_time": 0.3006595061079317, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.056, + "pct_cuda_time": 0.3006595061079317, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 524.321, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05437459153015785, + "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": 0.05437459153015785, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 675.254, + "cuda_time_us": 20.831, + "pct_cuda_time": 0.2974467216819113, + "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": 2.688, + "pct_cuda_time": 0.038382064609523196, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.863, + "pct_cuda_time": 0.24078748344880563, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.01827717362358247, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 154.819, + "cuda_time_us": 18.08, + "pct_cuda_time": 0.25816507743310235, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.968, + "pct_cuda_time": 0.22800774095419132, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.366, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04386521669659793, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04386521669659793, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 435.45, + "cuda_time_us": 133.79, + "pct_cuda_time": 1.9103930149211708, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 153.222, + "cuda_time_us": 80.479, + "pct_cuda_time": 1.1491630125408543, + "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": 80.479, + "pct_cuda_time": 1.1491630125408543, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.171, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.1311387207492042, + "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": 0.1311387207492042, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 134.952, + "cuda_time_us": 44.127, + "pct_cuda_time": 0.6300912816311124, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.127, + "pct_cuda_time": 0.6300912816311124, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2286.463, + "cuda_time_us": 204.25300000000001, + "pct_cuda_time": 2.916537143857493, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.618, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04295135801541881, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04295135801541881, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1623.733, + "cuda_time_us": 63.615, + "pct_cuda_time": 0.9083612500501554, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.591, + "cuda_time_us": 20.8, + "pct_cuda_time": 0.29700407138321516, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.8, + "pct_cuda_time": 0.29700407138321516, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 496.478, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05483152087074741, + "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": 0.05483152087074741, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 670.699, + "cuda_time_us": 21.216, + "pct_cuda_time": 0.3029441528108795, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.624, + "pct_cuda_time": 0.037468205928344066, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.28, + "pct_cuda_time": 0.24674184391836337, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.018734102964172033, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 161.413, + "cuda_time_us": 17.759, + "pct_cuda_time": 0.25358150498531334, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.647, + "pct_cuda_time": 0.2234241685064023, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.154, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.045692934058956185, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.045692934058956185, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 454.649, + "cuda_time_us": 134.43, + "pct_cuda_time": 1.9195316017329624, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 167.05, + "cuda_time_us": 81.279, + "pct_cuda_time": 1.1605862460555936, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.279, + "pct_cuda_time": 1.1605862460555936, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.921, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.12839714470566688, + "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": 0.12839714470566688, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 139.309, + "cuda_time_us": 44.159, + "pct_cuda_time": 0.6305482109717018, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.159, + "pct_cuda_time": 0.6305482109717018, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2306.195, + "cuda_time_us": 203.709, + "pct_cuda_time": 2.90876934506747, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.267, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04614986339954574, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04614986339954574, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1622.246, + "cuda_time_us": 63.615, + "pct_cuda_time": 0.9083612500501554, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 134.711, + "cuda_time_us": 21.023, + "pct_cuda_time": 0.30018829772544864, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.023, + "pct_cuda_time": 0.30018829772544864, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 498.575, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.05346073284897873, + "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": 0.05346073284897873, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 698.011, + "cuda_time_us": 21.056, + "pct_cuda_time": 0.3006595061079317, + "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": 0.03792513526893363, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 17.088, + "pct_cuda_time": 0.244000267874826, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.018734102964172033, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 152.512, + "cuda_time_us": 17.792, + "pct_cuda_time": 0.25405271336779633, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.68, + "pct_cuda_time": 0.22389537688888528, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.112, + "pct_cuda_time": 0.030157336478911077, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 99.877, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.044779075377777054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.044779075377777054, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 447.344, + "cuda_time_us": 133.726, + "pct_cuda_time": 1.9094791562399918, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.097, + "cuda_time_us": 80.895, + "pct_cuda_time": 1.1551030939685187, + "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": 80.895, + "pct_cuda_time": 1.1551030939685187, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.481, + "cuda_time_us": 9.088, + "pct_cuda_time": 0.12976793272743553, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.088, + "pct_cuda_time": 0.12976793272743553, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.982, + "cuda_time_us": 43.743, + "pct_cuda_time": 0.6246081295440375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.743, + "pct_cuda_time": 0.6246081295440375, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2259.884, + "cuda_time_us": 203.007, + "pct_cuda_time": 2.8987454576582867, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.229, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04295135801541881, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04295135801541881, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1611.057, + "cuda_time_us": 62.816, + "pct_cuda_time": 0.8969522955773098, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 138.135, + "cuda_time_us": 20.608, + "pct_cuda_time": 0.2942624953396778, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.2942624953396778, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[6, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 488.662, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.05391766218956829, + "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": 0.05391766218956829, + "trace": "_C::rotary_embedding(int64[6], bfloat16[6, 4096], bfloat16[6, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 685.672, + "cuda_time_us": 20.832, + "pct_cuda_time": 0.2974610007238047, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.624, + "pct_cuda_time": 0.037468205928344066, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[6], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 16.896, + "pct_cuda_time": 0.24125869183128862, + "trace": "_vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.018734102964172033, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[6, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[6, 1, 32, 128], None, None, None, None, int32[6], None, None, int32[6, 33], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[6, 32, 128], bfloat16[6, 8, 128], bfloat16[6, 8, 128], bfloat16[6, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 149.836, + "cuda_time_us": 17.6, + "pct_cuda_time": 0.251311137324259, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.52, + "pct_cuda_time": 0.22161073018593744, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.08, + "pct_cuda_time": 0.029700407138321516, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[6, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.54, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.04614986339954574, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04614986339954574, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 437.491, + "cuda_time_us": 133.951, + "pct_cuda_time": 1.9126919406660121, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 152.323, + "cuda_time_us": 81.439, + "pct_cuda_time": 1.1628708927585412, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.439, + "pct_cuda_time": 1.1628708927585412, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[6, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.812, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.12748328602448775, + "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": 0.12748328602448775, + "trace": "_C::silu_and_mul(bfloat16[6, 14336], bfloat16[6, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.74, + "cuda_time_us": 43.584, + "pct_cuda_time": 0.6223377618829832, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6223377618829832, + "trace": "mm(bfloat16[6, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[6, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[6, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.745, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04340828735600837, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::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": 0.04340828735600837, + "trace": "_C::fused_add_rms_norm(bfloat16[6, 4096], bfloat16[6, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 472.111, + "cuda_time_us": 349.78700000000003, + "pct_cuda_time": 4.994623226775033, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 4.96, + "pct_cuda_time": 0.07082404779138207, + "trace": "index_select(bfloat16[6, 4096], 0, int64[6])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.010966304174149483, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[6, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 344.059, + "pct_cuda_time": 4.912832874809501, + "trace": "mm(bfloat16[6, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[6, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[6, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 3097.422, + "cuda_time_us": 116.67, + "pct_cuda_time": 1.6659358177057555, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.010509374833559921, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.010509374833559921, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.011423233514739046, + "trace": "copy_(int32[6], int32[6], True) <- _to_copy(int32[6], 3, 0, None, None, True, None) <- to(int32[6], 3, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.010966304174149483, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.010966304174149483, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.767, + "pct_cuda_time": 0.01095202513225606, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.010966304174149483, + "trace": "copy_(bfloat16[6], bfloat16[6], True) <- _to_copy(bfloat16[6], 15, 0, None, None, True, None) <- to(bfloat16[6], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 4.353, + "pct_cuda_time": 0.06215666936207383, + "trace": "copy_(float32[6, 128256], bfloat16[6, 128256], False) <- _to_copy(bfloat16[6, 128256], 6, None, None, None, False, None) <- to(bfloat16[6, 128256], 6, False, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::elementwise_kernel<128, 4, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1})", + "cpu_time_us": 0, + "cuda_time_us": 5.536, + "pct_cuda_time": 0.07904877592199418, + "trace": "div_(float32[6, 128256], bfloat16[6, 1])" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::SoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.784, + "pct_cuda_time": 0.49668219322085366, + "trace": "_softmax(float32[6, 128256], -1, False) <- softmax(float32[6, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::LogSoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 28.255, + "pct_cuda_time": 0.4034543286986896, + "trace": "_log_softmax(float32[6, 128256], -1, False) <- log_softmax(float32[6, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 1.824, + "pct_cuda_time": 0.02604497241360502, + "trace": "copy_(int64[6], int32[6], False) <- _to_copy(int32[6], 4, None, None, None, False, None) <- to(int32[6], 4, False, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::index_elementwise_kernel<128, 4, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1}>(long, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1})", + "cpu_time_us": 0, + "cuda_time_us": 6.24, + "pct_cuda_time": 0.08910122141496456, + "trace": "index(float32[6, 128256], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, long, 4> >(at::native::ReduceOp, unsigned int, long, 4>)", + "cpu_time_us": 0, + "cuda_time_us": 27.712, + "pct_cuda_time": 0.3957008089505605, + "trace": "argmax(float32[6, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 2.623, + "pct_cuda_time": 0.03745392688645065, + "trace": "copy_(int64[6], int64[6], False) <- _to_copy(int64[6], 4, 0, None, None, False, None) <- to(int64[6], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + } +} \ No newline at end of file