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+ "pct_cuda_time": 0.05685617849286535, + "trace": "_C::rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2925.774, + "cuda_time_us": 170.75, + "pct_cuda_time": 0.7363616867154701, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 413.432, + "cuda_time_us": 88.159, + "pct_cuda_time": 0.3801868810491897, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 87.423, + "pct_cuda_time": 0.37701287108478215, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 967.397, + "cuda_time_us": 12.736, + "pct_cuda_time": 0.05492417242757382, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 12.736, + "pct_cuda_time": 0.05492417242757382, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1033.369, + "cuda_time_us": 22.4, + "pct_cuda_time": 0.09660030326457704, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.664, + "pct_cuda_time": 0.024426076682614484, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.456, + "pct_cuda_time": 0.06665420925255816, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.005520017329404403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 272.213, + "cuda_time_us": 47.455, + "pct_cuda_time": 0.20465032997412963, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.455, + "pct_cuda_time": 0.20465032997412963, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 108.724, + "cuda_time_us": 10.112, + "pct_cuda_time": 0.043608136902294786, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.112, + "pct_cuda_time": 0.043608136902294786, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 604.931, + "cuda_time_us": 517.432, + "pct_cuda_time": 2.231432505303421, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 194.58, + "cuda_time_us": 320.667, + "pct_cuda_time": 1.3828807788813449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0033120103976426417, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 319.899, + "pct_cuda_time": 1.3795687684837026, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 142.76, + "cuda_time_us": 45.855, + "pct_cuda_time": 0.1977503083123741, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.855, + "pct_cuda_time": 0.1977503083123741, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 198.104, + "cuda_time_us": 150.91, + "pct_cuda_time": 0.6508014181097019, + "trace": "" + }, + "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": 150.91, + "pct_cuda_time": 0.6508014181097019, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2571.233, + "cuda_time_us": 708.246, + "pct_cuda_time": 3.054320463657305, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.772, + "cuda_time_us": 9.984, + "pct_cuda_time": 0.04305613516935434, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.984, + "pct_cuda_time": 0.04305613516935434, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1830.217, + "cuda_time_us": 170.236, + "pct_cuda_time": 0.7341450547566312, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 160.708, + "cuda_time_us": 87.61500000000001, + "pct_cuda_time": 0.37784087368419284, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.879, + "pct_cuda_time": 0.3746668637197853, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 528.413, + "cuda_time_us": 12.672, + "pct_cuda_time": 0.054648171561103596, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 12.672, + "pct_cuda_time": 0.054648171561103596, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 799.426, + "cuda_time_us": 22.654, + "pct_cuda_time": 0.09769568170338074, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.727, + "pct_cuda_time": 0.024697765035546104, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.423, + "pct_cuda_time": 0.06651189630578445, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.006486020362050174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 187.344, + "cuda_time_us": 47.295, + "pct_cuda_time": 0.2039603278079541, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.295, + "pct_cuda_time": 0.2039603278079541, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.118, + "cuda_time_us": 9.92, + "pct_cuda_time": 0.042780134302884125, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.92, + "pct_cuda_time": 0.042780134302884125, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 500.185, + "cuda_time_us": 518.106, + "pct_cuda_time": 2.2343391394284358, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.553, + "cuda_time_us": 322.972, + "pct_cuda_time": 1.3928211225878113, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.236, + "pct_cuda_time": 1.389647112623404, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.402, + "cuda_time_us": 45.6, + "pct_cuda_time": 0.19665061736003184, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.6, + "pct_cuda_time": 0.19665061736003184, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 177.88, + "cuda_time_us": 149.534, + "pct_cuda_time": 0.6448673994805921, + "trace": "" + }, + "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": 149.534, + "pct_cuda_time": 0.6448673994805921, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2530.596, + "cuda_time_us": 708.2479999999999, + "pct_cuda_time": 3.054329088684382, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.964, + "cuda_time_us": 9.984, + "pct_cuda_time": 0.04305613516935434, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.984, + "pct_cuda_time": 0.04305613516935434, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1796.101, + "cuda_time_us": 170.911, + "pct_cuda_time": 0.7370560013951843, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 152.364, + "cuda_time_us": 87.423, + "pct_cuda_time": 0.37701287108478215, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.687, + "pct_cuda_time": 0.37383886112037457, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 529.777, + "cuda_time_us": 13.344, + "pct_cuda_time": 0.0575461806590409, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.344, + "pct_cuda_time": 0.0575461806590409, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 781.038, + "cuda_time_us": 22.721, + "pct_cuda_time": 0.09798462011046676, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.697, + "pct_cuda_time": 0.02456838962938819, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.552, + "pct_cuda_time": 0.06706821055226349, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.006348019928815063, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 181.543, + "cuda_time_us": 47.423, + "pct_cuda_time": 0.20451232954089454, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.423, + "pct_cuda_time": 0.20451232954089454, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.93, + "cuda_time_us": 10.048, + "pct_cuda_time": 0.043332136035824566, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.048, + "pct_cuda_time": 0.043332136035824566, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 480.399, + "cuda_time_us": 517.305, + "pct_cuda_time": 2.230884816084019, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 184.888, + "cuda_time_us": 322.364, + "pct_cuda_time": 1.3901991143563444, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 321.628, + "pct_cuda_time": 1.3870251043919368, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.653, + "cuda_time_us": 45.183, + "pct_cuda_time": 0.19485229921443684, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.183, + "pct_cuda_time": 0.19485229921443684, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.162, + "cuda_time_us": 149.758, + "pct_cuda_time": 0.6458334025132381, + "trace": "" + }, + "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": 149.758, + "pct_cuda_time": 0.6458334025132381, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2425.071, + "cuda_time_us": 707.7690000000001, + "pct_cuda_time": 3.052263394699395, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.838, + "cuda_time_us": 10.208, + "pct_cuda_time": 0.04402213820200011, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.208, + "pct_cuda_time": 0.04402213820200011, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1735.42, + "cuda_time_us": 170.01500000000001, + "pct_cuda_time": 0.7331919892646013, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.542, + "cuda_time_us": 87.295, + "pct_cuda_time": 0.3764608693518417, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0033120103976426417, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.527, + "pct_cuda_time": 0.3731488589541991, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 506.928, + "cuda_time_us": 13.056, + "pct_cuda_time": 0.056304176759924905, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.056, + "pct_cuda_time": 0.056304176759924905, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 738.892, + "cuda_time_us": 22.496000000000002, + "pct_cuda_time": 0.0970143045642824, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.76, + "pct_cuda_time": 0.02484007798231981, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.456, + "pct_cuda_time": 0.06665420925255816, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.005520017329404403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 199.654, + "cuda_time_us": 47.168, + "pct_cuda_time": 0.20341263858855227, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.168, + "pct_cuda_time": 0.20341263858855227, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.78, + "cuda_time_us": 9.953, + "pct_cuda_time": 0.04292244724965783, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.953, + "pct_cuda_time": 0.04292244724965783, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 463.455, + "cuda_time_us": 517.5930000000001, + "pct_cuda_time": 2.2321268199831357, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.84, + "cuda_time_us": 322.236, + "pct_cuda_time": 1.389647112623404, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.704, + "pct_cuda_time": 0.0030360095311724217, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 321.532, + "pct_cuda_time": 1.3866111030922315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.309, + "cuda_time_us": 45.759, + "pct_cuda_time": 0.1973363070126688, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.759, + "pct_cuda_time": 0.1973363070126688, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.217, + "cuda_time_us": 149.598, + "pct_cuda_time": 0.6451434003470624, + "trace": "" + }, + "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": 149.598, + "pct_cuda_time": 0.6451434003470624, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2439.352, + "cuda_time_us": 707.221, + "pct_cuda_time": 3.0499001372802432, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.011, + "cuda_time_us": 10.112, + "pct_cuda_time": 0.043608136902294786, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.112, + "pct_cuda_time": 0.043608136902294786, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1774.793, + "cuda_time_us": 169.373, + "pct_cuda_time": 0.7304233555728218, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 167.265, + "cuda_time_us": 86.558, + "pct_cuda_time": 0.3732825468738956, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 85.822, + "pct_cuda_time": 0.37010853690948803, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 519.633, + "cuda_time_us": 13.216, + "pct_cuda_time": 0.05699417892610045, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.216, + "pct_cuda_time": 0.05699417892610045, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 740.097, + "cuda_time_us": 22.464000000000002, + "pct_cuda_time": 0.09687630413104728, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.728, + "pct_cuda_time": 0.024702077549084704, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.424, + "pct_cuda_time": 0.06651620881932306, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.005658017762639513, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 176.471, + "cuda_time_us": 47.135, + "pct_cuda_time": 0.20327032564177852, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.135, + "pct_cuda_time": 0.20327032564177852, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.384, + "cuda_time_us": 9.888, + "pct_cuda_time": 0.04264213386964901, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.888, + "pct_cuda_time": 0.04264213386964901, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 447.287, + "cuda_time_us": 517.848, + "pct_cuda_time": 2.233226510935477, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.323, + "cuda_time_us": 322.491, + "pct_cuda_time": 1.3907468035757462, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 321.755, + "pct_cuda_time": 1.3875727936113387, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.769, + "cuda_time_us": 45.952, + "pct_cuda_time": 0.1981686221256181, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.952, + "pct_cuda_time": 0.1981686221256181, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.018, + "cuda_time_us": 149.405, + "pct_cuda_time": 0.6443110852341132, + "trace": "" + }, + "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": 149.405, + "pct_cuda_time": 0.6443110852341132, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2662.102, + "cuda_time_us": 707.6700000000001, + "pct_cuda_time": 3.0518364558590734, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.03, + "cuda_time_us": 9.952, + "pct_cuda_time": 0.04291813473611923, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.952, + "pct_cuda_time": 0.04291813473611923, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1936.555, + "cuda_time_us": 170.078, + "pct_cuda_time": 0.7334636776175328, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.027, + "cuda_time_us": 87.295, + "pct_cuda_time": 0.3764608693518417, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.559, + "pct_cuda_time": 0.37328685938743417, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 697.172, + "cuda_time_us": 12.512, + "pct_cuda_time": 0.05395816939492804, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 12.512, + "pct_cuda_time": 0.05395816939492804, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 744.333, + "cuda_time_us": 23.008, + "pct_cuda_time": 0.09922231149604413, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.792, + "pct_cuda_time": 0.02497807841555492, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.712, + "pct_cuda_time": 0.06775821271843904, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.006486020362050174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 184.687, + "cuda_time_us": 47.263, + "pct_cuda_time": 0.20382232737471898, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.263, + "pct_cuda_time": 0.20382232737471898, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.231, + "cuda_time_us": 10.112, + "pct_cuda_time": 0.043608136902294786, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.112, + "pct_cuda_time": 0.043608136902294786, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 465.939, + "cuda_time_us": 517.528, + "pct_cuda_time": 2.2318465066031266, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 176.492, + "cuda_time_us": 321.723, + "pct_cuda_time": 1.387434793178104, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.003169697450868934, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 320.988, + "pct_cuda_time": 1.3842650957272349, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.609, + "cuda_time_us": 46.207, + "pct_cuda_time": 0.19926831307796036, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 46.207, + "pct_cuda_time": 0.19926831307796036, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 139.464, + "cuda_time_us": 149.598, + "pct_cuda_time": 0.6451434003470624, + "trace": "" + }, + "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": 149.598, + "pct_cuda_time": 0.6451434003470624, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2381.744, + "cuda_time_us": 708.885, + "pct_cuda_time": 3.0570761598084686, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.398, + "cuda_time_us": 10.207, + "pct_cuda_time": 0.04401782568846152, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.207, + "pct_cuda_time": 0.04401782568846152, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1654.683, + "cuda_time_us": 169.949, + "pct_cuda_time": 0.7329073633710538, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 147.11, + "cuda_time_us": 87.42200000000001, + "pct_cuda_time": 0.3770085585712436, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.686, + "pct_cuda_time": 0.373834548606836, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 460.843, + "cuda_time_us": 12.736, + "pct_cuda_time": 0.05492417242757382, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 12.736, + "pct_cuda_time": 0.05492417242757382, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 722.929, + "cuda_time_us": 22.624, + "pct_cuda_time": 0.09756630629722281, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.792, + "pct_cuda_time": 0.02497807841555492, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.328, + "pct_cuda_time": 0.06610220751961772, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.006486020362050174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 183.527, + "cuda_time_us": 47.167, + "pct_cuda_time": 0.20340832607501366, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.167, + "pct_cuda_time": 0.20340832607501366, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 113.646, + "cuda_time_us": 9.76, + "pct_cuda_time": 0.04209013213670857, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.76, + "pct_cuda_time": 0.04209013213670857, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 472.231, + "cuda_time_us": 518.969, + "pct_cuda_time": 2.2380608386122454, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 168.201, + "cuda_time_us": 322.94, + "pct_cuda_time": 1.3926831221545766, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.204, + "pct_cuda_time": 1.389509112190169, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.678, + "cuda_time_us": 45.791, + "pct_cuda_time": 0.19747430744590388, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.791, + "pct_cuda_time": 0.19747430744590388, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.16, + "cuda_time_us": 150.238, + "pct_cuda_time": 0.6479034090117646, + "trace": "" + }, + "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": 150.238, + "pct_cuda_time": 0.6479034090117646, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2384.809, + "cuda_time_us": 708.2779999999999, + "pct_cuda_time": 3.05445846409054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.64, + "cuda_time_us": 9.824, + "pct_cuda_time": 0.04236613300317879, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.824, + "pct_cuda_time": 0.04236613300317879, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1711.564, + "cuda_time_us": 170.974, + "pct_cuda_time": 0.7373276897481159, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 152.927, + "cuda_time_us": 88.447, + "pct_cuda_time": 0.3814288849483057, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0033120103976426417, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 87.679, + "pct_cuda_time": 0.378116874550663, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 526.963, + "cuda_time_us": 12.8, + "pct_cuda_time": 0.05520017329404403, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 12.8, + "pct_cuda_time": 0.05520017329404403, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 714.094, + "cuda_time_us": 22.496000000000002, + "pct_cuda_time": 0.0970143045642824, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.76, + "pct_cuda_time": 0.02484007798231981, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.456, + "pct_cuda_time": 0.06665420925255816, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.005520017329404403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 175.123, + "cuda_time_us": 47.231, + "pct_cuda_time": 0.2036843269414839, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.231, + "pct_cuda_time": 0.2036843269414839, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.104, + "cuda_time_us": 10.112, + "pct_cuda_time": 0.043608136902294786, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.112, + "pct_cuda_time": 0.043608136902294786, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 457.756, + "cuda_time_us": 517.3679999999999, + "pct_cuda_time": 2.231156504436951, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.79, + "cuda_time_us": 322.811, + "pct_cuda_time": 1.3921268079080973, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.075, + "pct_cuda_time": 1.3889527979436898, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.623, + "cuda_time_us": 45.375, + "pct_cuda_time": 0.19568030181384746, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.375, + "pct_cuda_time": 0.19568030181384746, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 152.854, + "cuda_time_us": 149.182, + "pct_cuda_time": 0.643349394715006, + "trace": "" + }, + "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": 149.182, + "pct_cuda_time": 0.643349394715006, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2375.021, + "cuda_time_us": 707.9580000000001, + "pct_cuda_time": 3.0530784597581895, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.198, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1634.589, + "cuda_time_us": 170.237, + "pct_cuda_time": 0.7341493672701698, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 160.084, + "cuda_time_us": 86.975, + "pct_cuda_time": 0.37508086501949056, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0033120103976426417, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.207, + "pct_cuda_time": 0.3717688546218479, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 469.186, + "cuda_time_us": 13.28, + "pct_cuda_time": 0.05727017979257067, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.28, + "pct_cuda_time": 0.05727017979257067, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 686.892, + "cuda_time_us": 22.687, + "pct_cuda_time": 0.09783799465015446, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.792, + "pct_cuda_time": 0.02497807841555492, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.583, + "pct_cuda_time": 0.06720189847196001, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.005658017762639513, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 168.665, + "cuda_time_us": 47.295, + "pct_cuda_time": 0.2039603278079541, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.295, + "pct_cuda_time": 0.2039603278079541, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.396, + "cuda_time_us": 10.015, + "pct_cuda_time": 0.04318982308905086, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.015, + "pct_cuda_time": 0.04318982308905086, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 535.917, + "cuda_time_us": 517.69, + "pct_cuda_time": 2.2325451337963798, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.785, + "cuda_time_us": 323.03700000000003, + "pct_cuda_time": 1.3931014359678207, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.003178322477946129, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.3, + "pct_cuda_time": 1.3899231134898744, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 113.046, + "cuda_time_us": 45.247, + "pct_cuda_time": 0.19512830008090706, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.247, + "pct_cuda_time": 0.19512830008090706, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 218.032, + "cuda_time_us": 149.406, + "pct_cuda_time": 0.6443153977476518, + "trace": "" + }, + "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": 149.406, + "pct_cuda_time": 0.6443153977476518, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2384.079, + "cuda_time_us": 707.034, + "pct_cuda_time": 3.0490936972485256, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.706, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1718.637, + "cuda_time_us": 169.88799999999998, + "pct_cuda_time": 0.7326443000451993, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.504, + "cuda_time_us": 87.263, + "pct_cuda_time": 0.37632286891860656, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.527, + "pct_cuda_time": 0.3731488589541991, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 504.67, + "cuda_time_us": 12.8, + "pct_cuda_time": 0.05520017329404403, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 12.8, + "pct_cuda_time": 0.05520017329404403, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 739.582, + "cuda_time_us": 22.720999999999997, + "pct_cuda_time": 0.09798462011046674, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.728, + "pct_cuda_time": 0.024702077549084704, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.52, + "pct_cuda_time": 0.06693021011902839, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.473, + "pct_cuda_time": 0.006352332442353661, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 178.287, + "cuda_time_us": 47.104, + "pct_cuda_time": 0.20313663772208201, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.104, + "pct_cuda_time": 0.20313663772208201, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.632, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 447.507, + "cuda_time_us": 517.114, + "pct_cuda_time": 2.230061125998147, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 156.496, + "cuda_time_us": 322.108, + "pct_cuda_time": 1.3890951108904637, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 321.372, + "pct_cuda_time": 1.3859211009260561, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.051, + "cuda_time_us": 45.184, + "pct_cuda_time": 0.19485661172797542, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.184, + "pct_cuda_time": 0.19485661172797542, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.78, + "cuda_time_us": 149.822, + "pct_cuda_time": 0.6461094033797082, + "trace": "" + }, + "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": 149.822, + "pct_cuda_time": 0.6461094033797082, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2362.982, + "cuda_time_us": 706.677, + "pct_cuda_time": 3.0475541299152464, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.715, + "cuda_time_us": 9.951, + "pct_cuda_time": 0.04291382222258064, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.951, + "pct_cuda_time": 0.04291382222258064, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1701.54, + "cuda_time_us": 169.62900000000002, + "pct_cuda_time": 0.7315273590387028, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.04, + "cuda_time_us": 86.71900000000001, + "pct_cuda_time": 0.37397686155360976, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 85.983, + "pct_cuda_time": 0.3708028515892022, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 461.207, + "cuda_time_us": 13.024, + "pct_cuda_time": 0.0561661763266898, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.024, + "pct_cuda_time": 0.0561661763266898, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 787.06, + "cuda_time_us": 22.624, + "pct_cuda_time": 0.09756630629722281, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.76, + "pct_cuda_time": 0.02484007798231981, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.424, + "pct_cuda_time": 0.06651620881932306, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.44, + "pct_cuda_time": 0.006210019495579953, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 178.315, + "cuda_time_us": 47.262, + "pct_cuda_time": 0.2038180148611804, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.262, + "pct_cuda_time": 0.2038180148611804, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.399, + "cuda_time_us": 9.663, + "pct_cuda_time": 0.04167181832346465, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.663, + "pct_cuda_time": 0.04167181832346465, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 446.051, + "cuda_time_us": 517.434, + "pct_cuda_time": 2.231441130330498, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 153.72, + "cuda_time_us": 322.68399999999997, + "pct_cuda_time": 1.3915791186886954, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 321.948, + "pct_cuda_time": 1.388405108724288, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.578, + "cuda_time_us": 45.728, + "pct_cuda_time": 0.1972026190929723, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.728, + "pct_cuda_time": 0.1972026190929723, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.205, + "cuda_time_us": 149.022, + "pct_cuda_time": 0.6426593925488303, + "trace": "" + }, + "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": 149.022, + "pct_cuda_time": 0.6426593925488303, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2422.099, + "cuda_time_us": 708.023, + "pct_cuda_time": 3.053358773138198, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.596, + "cuda_time_us": 10.111, + "pct_cuda_time": 0.043603824388756186, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.111, + "pct_cuda_time": 0.043603824388756186, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1752.012, + "cuda_time_us": 170.206, + "pct_cuda_time": 0.7340156793504732, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.976, + "cuda_time_us": 86.815, + "pct_cuda_time": 0.374390862853315, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.079, + "pct_cuda_time": 0.37121685288890743, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 483.206, + "cuda_time_us": 13.248, + "pct_cuda_time": 0.05713217935933557, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.248, + "pct_cuda_time": 0.05713217935933557, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 706.164, + "cuda_time_us": 23.008, + "pct_cuda_time": 0.09922231149604413, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.76, + "pct_cuda_time": 0.02484007798231981, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.808, + "pct_cuda_time": 0.06817221401814438, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.44, + "pct_cuda_time": 0.006210019495579953, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 187.536, + "cuda_time_us": 47.135, + "pct_cuda_time": 0.20327032564177852, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.135, + "pct_cuda_time": 0.20327032564177852, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.934, + "cuda_time_us": 10.048, + "pct_cuda_time": 0.043332136035824566, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.048, + "pct_cuda_time": 0.043332136035824566, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 453.989, + "cuda_time_us": 517.658, + "pct_cuda_time": 2.2324071333631443, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 162.914, + "cuda_time_us": 322.812, + "pct_cuda_time": 1.3921311204216362, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.076, + "pct_cuda_time": 1.3889571104572287, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.874, + "cuda_time_us": 45.567, + "pct_cuda_time": 0.19650830441325814, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.567, + "pct_cuda_time": 0.19650830441325814, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 139.738, + "cuda_time_us": 149.279, + "pct_cuda_time": 0.6437677085282499, + "trace": "" + }, + "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": 149.279, + "pct_cuda_time": 0.6437677085282499, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2680.91, + "cuda_time_us": 708.4069999999999, + "pct_cuda_time": 3.055014778337019, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.453, + "cuda_time_us": 10.144, + "pct_cuda_time": 0.04374613733552989, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.144, + "pct_cuda_time": 0.04374613733552989, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1999.128, + "cuda_time_us": 169.566, + "pct_cuda_time": 0.7312556706857711, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.758, + "cuda_time_us": 86.559, + "pct_cuda_time": 0.37328685938743417, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 85.823, + "pct_cuda_time": 0.3701128494230266, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 569.144, + "cuda_time_us": 13.024, + "pct_cuda_time": 0.0561661763266898, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.024, + "pct_cuda_time": 0.0561661763266898, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 958.955, + "cuda_time_us": 22.719, + "pct_cuda_time": 0.09797599508338956, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.76, + "pct_cuda_time": 0.02484007798231981, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.647, + "pct_cuda_time": 0.06747789933843024, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.005658017762639513, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 184.986, + "cuda_time_us": 47.264, + "pct_cuda_time": 0.20382663988825758, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.264, + "pct_cuda_time": 0.20382663988825758, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.11, + "cuda_time_us": 9.984, + "pct_cuda_time": 0.04305613516935434, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.984, + "pct_cuda_time": 0.04305613516935434, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 459.839, + "cuda_time_us": 518.713, + "pct_cuda_time": 2.236956835146364, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.386, + "cuda_time_us": 323.324, + "pct_cuda_time": 1.3943391273533978, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.588, + "pct_cuda_time": 1.3911651173889903, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.746, + "cuda_time_us": 45.151, + "pct_cuda_time": 0.19471429878120172, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.151, + "pct_cuda_time": 0.19471429878120172, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 139.548, + "cuda_time_us": 150.238, + "pct_cuda_time": 0.6479034090117646, + "trace": "" + }, + "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": 150.238, + "pct_cuda_time": 0.6479034090117646, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2468.975, + "cuda_time_us": 707.223, + "pct_cuda_time": 3.04990876230732, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.413, + "cuda_time_us": 10.048, + "pct_cuda_time": 0.043332136035824566, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.048, + "pct_cuda_time": 0.043332136035824566, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1774.15, + "cuda_time_us": 170.333, + "pct_cuda_time": 0.7345633685698751, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 143.637, + "cuda_time_us": 87.006, + "pct_cuda_time": 0.3752145529391871, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.003169697450868934, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.271, + "pct_cuda_time": 0.37204485548831817, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 520.549, + "cuda_time_us": 13.088, + "pct_cuda_time": 0.05644217719316002, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.088, + "pct_cuda_time": 0.05644217719316002, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 742.584, + "cuda_time_us": 22.88, + "pct_cuda_time": 0.09867030976310369, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.792, + "pct_cuda_time": 0.02497807841555492, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.583, + "pct_cuda_time": 0.06720189847196001, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0064903328755887705, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 219.299, + "cuda_time_us": 47.359, + "pct_cuda_time": 0.20423632867442432, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.359, + "pct_cuda_time": 0.20423632867442432, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 91.499, + "cuda_time_us": 10.048, + "pct_cuda_time": 0.043332136035824566, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.048, + "pct_cuda_time": 0.043332136035824566, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 467.807, + "cuda_time_us": 516.794, + "pct_cuda_time": 2.228681121665796, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 169.463, + "cuda_time_us": 322.46, + "pct_cuda_time": 1.3906131156560497, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 321.724, + "pct_cuda_time": 1.3874391056916422, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.467, + "cuda_time_us": 45.024, + "pct_cuda_time": 0.19416660956179987, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.024, + "pct_cuda_time": 0.19416660956179987, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 145.251, + "cuda_time_us": 149.31, + "pct_cuda_time": 0.6439013964479464, + "trace": "" + }, + "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": 149.31, + "pct_cuda_time": 0.6439013964479464, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2401.015, + "cuda_time_us": 706.1960000000001, + "pct_cuda_time": 3.0454798109031818, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.904, + "cuda_time_us": 10.047, + "pct_cuda_time": 0.043327823522285966, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.047, + "pct_cuda_time": 0.043327823522285966, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1714.162, + "cuda_time_us": 170.04399999999998, + "pct_cuda_time": 0.7333170521572204, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.249, + "cuda_time_us": 87.007, + "pct_cuda_time": 0.3752188654527257, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.271, + "pct_cuda_time": 0.37204485548831817, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 512.62, + "cuda_time_us": 13.183, + "pct_cuda_time": 0.05685186597932675, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.183, + "pct_cuda_time": 0.05685186597932675, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 730.122, + "cuda_time_us": 22.719, + "pct_cuda_time": 0.09797599508338956, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.759, + "pct_cuda_time": 0.024835765468781214, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.488, + "pct_cuda_time": 0.06679220968579327, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.006348019928815063, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 174.865, + "cuda_time_us": 47.135, + "pct_cuda_time": 0.20327032564177852, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.135, + "pct_cuda_time": 0.20327032564177852, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.445, + "cuda_time_us": 9.888, + "pct_cuda_time": 0.04264213386964901, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.888, + "pct_cuda_time": 0.04264213386964901, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 470.55, + "cuda_time_us": 516.2170000000001, + "pct_cuda_time": 2.226192801354026, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.859, + "cuda_time_us": 322.396, + "pct_cuda_time": 1.3903371147895798, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 321.66, + "pct_cuda_time": 1.387163104825172, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 117.993, + "cuda_time_us": 44.927, + "pct_cuda_time": 0.19374829574855595, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 44.927, + "pct_cuda_time": 0.19374829574855595, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.46, + "cuda_time_us": 148.894, + "pct_cuda_time": 0.64210739081589, + "trace": "" + }, + "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": 148.894, + "pct_cuda_time": 0.64210739081589, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2419.625, + "cuda_time_us": 708.344, + "pct_cuda_time": 3.054743089984088, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.423, + "cuda_time_us": 9.856, + "pct_cuda_time": 0.0425041334364139, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.856, + "pct_cuda_time": 0.0425041334364139, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1726.859, + "cuda_time_us": 169.95000000000002, + "pct_cuda_time": 0.7329116758845925, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.416, + "cuda_time_us": 87.35900000000001, + "pct_cuda_time": 0.3767368702183119, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.623, + "pct_cuda_time": 0.3735628602539044, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 510.797, + "cuda_time_us": 12.864, + "pct_cuda_time": 0.05547617416051425, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 12.864, + "pct_cuda_time": 0.05547617416051425, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 727.32, + "cuda_time_us": 22.399, + "pct_cuda_time": 0.09659599075103846, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.696, + "pct_cuda_time": 0.024564077115849594, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.327, + "pct_cuda_time": 0.06609789500607913, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.005934018629109732, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 194.689, + "cuda_time_us": 47.328, + "pct_cuda_time": 0.2041026407547278, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.328, + "pct_cuda_time": 0.2041026407547278, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.358, + "cuda_time_us": 10.081, + "pct_cuda_time": 0.04347444898259827, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.081, + "pct_cuda_time": 0.04347444898259827, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 475.577, + "cuda_time_us": 518.457, + "pct_cuda_time": 2.235852831680483, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 174.805, + "cuda_time_us": 323.355, + "pct_cuda_time": 1.3944728152730945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.619, + "pct_cuda_time": 1.391298805308687, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.409, + "cuda_time_us": 45.408, + "pct_cuda_time": 0.19582261476062118, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.408, + "pct_cuda_time": 0.19582261476062118, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 145.991, + "cuda_time_us": 149.694, + "pct_cuda_time": 0.6455574016467677, + "trace": "" + }, + "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": 149.694, + "pct_cuda_time": 0.6455574016467677, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2370.301, + "cuda_time_us": 709.8800000000001, + "pct_cuda_time": 3.0613671107793734, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.364, + "cuda_time_us": 10.912, + "pct_cuda_time": 0.04705814773317254, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.912, + "pct_cuda_time": 0.04705814773317254, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1669.657, + "cuda_time_us": 170.335, + "pct_cuda_time": 0.7345719935969524, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 153.652, + "cuda_time_us": 87.552, + "pct_cuda_time": 0.37756918533126116, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.816, + "pct_cuda_time": 0.3743951753668537, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 482.368, + "cuda_time_us": 13.152, + "pct_cuda_time": 0.05671817805963024, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.152, + "pct_cuda_time": 0.05671817805963024, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 718.82, + "cuda_time_us": 22.464000000000002, + "pct_cuda_time": 0.09687630413104728, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.696, + "pct_cuda_time": 0.024564077115849594, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.456, + "pct_cuda_time": 0.06665420925255816, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.005658017762639513, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 174.448, + "cuda_time_us": 47.167, + "pct_cuda_time": 0.20340832607501366, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.167, + "pct_cuda_time": 0.20340832607501366, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.156, + "cuda_time_us": 9.856, + "pct_cuda_time": 0.0425041334364139, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.856, + "pct_cuda_time": 0.0425041334364139, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 479.805, + "cuda_time_us": 518.777, + "pct_cuda_time": 2.2372328360128346, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 156.484, + "cuda_time_us": 323.03499999999997, + "pct_cuda_time": 1.3930928109407432, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.299, + "pct_cuda_time": 1.3899188009763357, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.92, + "cuda_time_us": 45.728, + "pct_cuda_time": 0.1972026190929723, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.728, + "pct_cuda_time": 0.1972026190929723, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 174.154, + "cuda_time_us": 150.014, + "pct_cuda_time": 0.6469374059791189, + "trace": "" + }, + "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": 150.014, + "pct_cuda_time": 0.6469374059791189, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2339.818, + "cuda_time_us": 708.951, + "pct_cuda_time": 3.0573607857020164, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.587, + "cuda_time_us": 10.048, + "pct_cuda_time": 0.043332136035824566, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.048, + "pct_cuda_time": 0.043332136035824566, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1656.121, + "cuda_time_us": 170.748, + "pct_cuda_time": 0.736353061688393, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.408, + "cuda_time_us": 87.23100000000001, + "pct_cuda_time": 0.37618486848537147, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.495, + "pct_cuda_time": 0.37301085852096394, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 471.435, + "cuda_time_us": 13.055, + "pct_cuda_time": 0.05629986424638631, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.055, + "pct_cuda_time": 0.05629986424638631, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 718.575, + "cuda_time_us": 22.911, + "pct_cuda_time": 0.09880399768280022, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.888, + "pct_cuda_time": 0.025392079715260252, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.52, + "pct_cuda_time": 0.06693021011902839, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.006481707848511576, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 173.012, + "cuda_time_us": 47.551, + "pct_cuda_time": 0.205064331273835, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.551, + "pct_cuda_time": 0.205064331273835, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.81, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 450.079, + "cuda_time_us": 518.139, + "pct_cuda_time": 2.2344814523752095, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 155.61, + "cuda_time_us": 323.164, + "pct_cuda_time": 1.393649125187222, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.428, + "pct_cuda_time": 1.3904751152228148, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 107.855, + "cuda_time_us": 45.696, + "pct_cuda_time": 0.19706461865973715, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.696, + "pct_cuda_time": 0.19706461865973715, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.694, + "cuda_time_us": 149.279, + "pct_cuda_time": 0.6437677085282499, + "trace": "" + }, + "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": 149.279, + "pct_cuda_time": 0.6437677085282499, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2363.281, + "cuda_time_us": 709.75, + "pct_cuda_time": 3.0608064840193556, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.097, + "cuda_time_us": 9.728, + "pct_cuda_time": 0.041952131703473464, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.728, + "pct_cuda_time": 0.041952131703473464, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1677.357, + "cuda_time_us": 170.686, + "pct_cuda_time": 0.7360856858489999, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.075, + "cuda_time_us": 87.232, + "pct_cuda_time": 0.37618918099891, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.496, + "pct_cuda_time": 0.3730151710345025, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 454.856, + "cuda_time_us": 13.28, + "pct_cuda_time": 0.05727017979257067, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.28, + "pct_cuda_time": 0.05727017979257067, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 715.876, + "cuda_time_us": 22.783, + "pct_cuda_time": 0.09825199594985978, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.76, + "pct_cuda_time": 0.02484007798231981, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.583, + "pct_cuda_time": 0.06720189847196001, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.44, + "pct_cuda_time": 0.006210019495579953, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 190.72, + "cuda_time_us": 47.391, + "pct_cuda_time": 0.2043743291076594, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.391, + "pct_cuda_time": 0.2043743291076594, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.926, + "cuda_time_us": 9.984, + "pct_cuda_time": 0.04305613516935434, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.984, + "pct_cuda_time": 0.04305613516935434, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.917, + "cuda_time_us": 519.352, + "pct_cuda_time": 2.2397125312975277, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 179.445, + "cuda_time_us": 324.218, + "pct_cuda_time": 1.398194514456904, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.003169697450868934, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 323.483, + "pct_cuda_time": 1.3950248170060349, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.285, + "cuda_time_us": 45.407, + "pct_cuda_time": 0.19581830224708255, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.407, + "pct_cuda_time": 0.19581830224708255, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.509, + "cuda_time_us": 149.727, + "pct_cuda_time": 0.6456997145935415, + "trace": "" + }, + "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": 149.727, + "pct_cuda_time": 0.6456997145935415, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2693.815, + "cuda_time_us": 706.808, + "pct_cuda_time": 3.0481190691888025, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.349, + "cuda_time_us": 9.952, + "pct_cuda_time": 0.04291813473611923, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.952, + "pct_cuda_time": 0.04291813473611923, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2016.569, + "cuda_time_us": 170.526, + "pct_cuda_time": 0.7353956836828245, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 147.144, + "cuda_time_us": 87.71, + "pct_cuda_time": 0.3782505624703595, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.003169697450868934, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.975, + "pct_cuda_time": 0.37508086501949056, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 507.227, + "cuda_time_us": 13.152, + "pct_cuda_time": 0.05671817805963024, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.152, + "pct_cuda_time": 0.05671817805963024, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1040.171, + "cuda_time_us": 22.625, + "pct_cuda_time": 0.09757061881076143, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.921, + "pct_cuda_time": 0.02553439266203396, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.424, + "pct_cuda_time": 0.06651620881932306, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.005520017329404403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 183.719, + "cuda_time_us": 47.039, + "pct_cuda_time": 0.2028563243420732, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.039, + "pct_cuda_time": 0.2028563243420732, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.404, + "cuda_time_us": 9.76, + "pct_cuda_time": 0.04209013213670857, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.76, + "pct_cuda_time": 0.04209013213670857, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 452.035, + "cuda_time_us": 516.5699999999999, + "pct_cuda_time": 2.2277151186331503, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.696, + "cuda_time_us": 322.108, + "pct_cuda_time": 1.3890951108904637, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 321.372, + "pct_cuda_time": 1.3859211009260561, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.882, + "cuda_time_us": 45.056, + "pct_cuda_time": 0.194304609995035, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.056, + "pct_cuda_time": 0.194304609995035, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.586, + "cuda_time_us": 149.406, + "pct_cuda_time": 0.6443153977476518, + "trace": "" + }, + "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": 149.406, + "pct_cuda_time": 0.6443153977476518, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2360.862, + "cuda_time_us": 708.5980000000001, + "pct_cuda_time": 3.0558384684228916, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.422, + "cuda_time_us": 9.92, + "pct_cuda_time": 0.042780134302884125, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.92, + "pct_cuda_time": 0.042780134302884125, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1705.928, + "cuda_time_us": 169.981, + "pct_cuda_time": 0.7330453638042889, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 147.561, + "cuda_time_us": 86.879, + "pct_cuda_time": 0.3746668637197853, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.143, + "pct_cuda_time": 0.3714928537553777, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 502.169, + "cuda_time_us": 13.279, + "pct_cuda_time": 0.05726586727903208, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.279, + "pct_cuda_time": 0.05726586727903208, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 707.761, + "cuda_time_us": 22.687, + "pct_cuda_time": 0.09783799465015446, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.696, + "pct_cuda_time": 0.024564077115849594, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.679, + "pct_cuda_time": 0.06761589977166534, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.005658017762639513, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 180.237, + "cuda_time_us": 47.136, + "pct_cuda_time": 0.20327463815531716, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.136, + "pct_cuda_time": 0.20327463815531716, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 88.637, + "cuda_time_us": 9.759, + "pct_cuda_time": 0.04208581962316998, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.759, + "pct_cuda_time": 0.04208581962316998, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 434.746, + "cuda_time_us": 518.9380000000001, + "pct_cuda_time": 2.237927150692549, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 156.908, + "cuda_time_us": 322.845, + "pct_cuda_time": 1.39227343336841, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.003178322477946129, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.108, + "pct_cuda_time": 1.3890951108904637, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.907, + "cuda_time_us": 45.951, + "pct_cuda_time": 0.19816430961207948, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.951, + "pct_cuda_time": 0.19816430961207948, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 134.22, + "cuda_time_us": 150.142, + "pct_cuda_time": 0.6474894077120592, + "trace": "" + }, + "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": 150.142, + "pct_cuda_time": 0.6474894077120592, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2314.442, + "cuda_time_us": 708.856, + "pct_cuda_time": 3.0569510969158493, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.429, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1619.379, + "cuda_time_us": 169.75900000000001, + "pct_cuda_time": 0.7320879857987205, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 164.019, + "cuda_time_us": 86.495, + "pct_cuda_time": 0.37301085852096394, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 85.759, + "pct_cuda_time": 0.3698368485565564, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 454.543, + "cuda_time_us": 13.024, + "pct_cuda_time": 0.0561661763266898, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.024, + "pct_cuda_time": 0.0561661763266898, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 705.155, + "cuda_time_us": 22.752, + "pct_cuda_time": 0.09811830803016326, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.728, + "pct_cuda_time": 0.024702077549084704, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.52, + "pct_cuda_time": 0.06693021011902839, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.006486020362050174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 166.283, + "cuda_time_us": 47.488, + "pct_cuda_time": 0.20479264292090335, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.488, + "pct_cuda_time": 0.20479264292090335, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.486, + "cuda_time_us": 10.144, + "pct_cuda_time": 0.04374613733552989, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.144, + "pct_cuda_time": 0.04374613733552989, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.032, + "cuda_time_us": 518.937, + "pct_cuda_time": 2.23792283817901, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 178.289, + "cuda_time_us": 323.387, + "pct_cuda_time": 1.3946108157063293, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.651, + "pct_cuda_time": 1.3914368057419217, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.983, + "cuda_time_us": 45.824, + "pct_cuda_time": 0.19761662039267763, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.824, + "pct_cuda_time": 0.19761662039267763, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.677, + "cuda_time_us": 149.726, + "pct_cuda_time": 0.6456954020800029, + "trace": "" + }, + "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": 149.726, + "pct_cuda_time": 0.6456954020800029, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2380.018, + "cuda_time_us": 708.4399999999999, + "pct_cuda_time": 3.055157091283793, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.005, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1720.728, + "cuda_time_us": 170.752, + "pct_cuda_time": 0.7363703117425474, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.271, + "cuda_time_us": 87.295, + "pct_cuda_time": 0.3764608693518417, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.559, + "pct_cuda_time": 0.37328685938743417, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 513.336, + "cuda_time_us": 13.12, + "pct_cuda_time": 0.056580177626395126, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.12, + "pct_cuda_time": 0.056580177626395126, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 745.679, + "cuda_time_us": 22.849, + "pct_cuda_time": 0.0985366218434072, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.632, + "pct_cuda_time": 0.024288076249379374, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.712, + "pct_cuda_time": 0.06775821271843904, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0064903328755887705, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 172.813, + "cuda_time_us": 47.488, + "pct_cuda_time": 0.20479264292090335, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.488, + "pct_cuda_time": 0.20479264292090335, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.938, + "cuda_time_us": 9.824, + "pct_cuda_time": 0.04236613300317879, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.824, + "pct_cuda_time": 0.04236613300317879, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 447.006, + "cuda_time_us": 517.848, + "pct_cuda_time": 2.233226510935477, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.948, + "cuda_time_us": 322.683, + "pct_cuda_time": 1.391574806175157, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0033120103976426417, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 321.915, + "pct_cuda_time": 1.3882627957775144, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.964, + "cuda_time_us": 45.247, + "pct_cuda_time": 0.19512830008090706, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.247, + "pct_cuda_time": 0.19512830008090706, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.392, + "cuda_time_us": 149.918, + "pct_cuda_time": 0.6465234046794136, + "trace": "" + }, + "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": 149.918, + "pct_cuda_time": 0.6465234046794136, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2349.799, + "cuda_time_us": 709.0459999999999, + "pct_cuda_time": 3.0577704744881826, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.078, + "cuda_time_us": 9.664, + "pct_cuda_time": 0.041676130837003236, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.664, + "pct_cuda_time": 0.041676130837003236, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1697.614, + "cuda_time_us": 170.334, + "pct_cuda_time": 0.7345676810834137, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.348, + "cuda_time_us": 87.99900000000001, + "pct_cuda_time": 0.37949687888301414, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 87.263, + "pct_cuda_time": 0.37632286891860656, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 491.126, + "cuda_time_us": 12.768, + "pct_cuda_time": 0.05506217286080892, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 12.768, + "pct_cuda_time": 0.05506217286080892, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 739.977, + "cuda_time_us": 22.4, + "pct_cuda_time": 0.09660030326457704, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.664, + "pct_cuda_time": 0.024426076682614484, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.456, + "pct_cuda_time": 0.06665420925255816, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.005520017329404403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 172.382, + "cuda_time_us": 47.167, + "pct_cuda_time": 0.20340832607501366, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.167, + "pct_cuda_time": 0.20340832607501366, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.054, + "cuda_time_us": 9.888, + "pct_cuda_time": 0.04264213386964901, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.888, + "pct_cuda_time": 0.04264213386964901, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 437.523, + "cuda_time_us": 519.16, + "pct_cuda_time": 2.238884528698117, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 155.836, + "cuda_time_us": 323.54699999999997, + "pct_cuda_time": 1.3953008178725048, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.811, + "pct_cuda_time": 1.3921268079080973, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.677, + "cuda_time_us": 45.727, + "pct_cuda_time": 0.19719830657943366, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.727, + "pct_cuda_time": 0.19719830657943366, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 135.614, + "cuda_time_us": 149.886, + "pct_cuda_time": 0.6463854042461784, + "trace": "" + }, + "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": 149.886, + "pct_cuda_time": 0.6463854042461784, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2377.52, + "cuda_time_us": 708.8199999999999, + "pct_cuda_time": 3.05679584642846, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.138, + "cuda_time_us": 9.888, + "pct_cuda_time": 0.04264213386964901, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.888, + "pct_cuda_time": 0.04264213386964901, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1693.328, + "cuda_time_us": 169.373, + "pct_cuda_time": 0.7304233555728218, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 143.668, + "cuda_time_us": 85.983, + "pct_cuda_time": 0.3708028515892022, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0033120103976426417, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 85.215, + "pct_cuda_time": 0.36749084119155956, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 482.07, + "cuda_time_us": 13.6, + "pct_cuda_time": 0.058650184124921775, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.6, + "pct_cuda_time": 0.058650184124921775, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 737.671, + "cuda_time_us": 22.751, + "pct_cuda_time": 0.09811399551662467, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.888, + "pct_cuda_time": 0.025392079715260252, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.583, + "pct_cuda_time": 0.06720189847196001, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.005520017329404403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 189.514, + "cuda_time_us": 47.039, + "pct_cuda_time": 0.2028563243420732, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.039, + "pct_cuda_time": 0.2028563243420732, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.162, + "cuda_time_us": 9.855, + "pct_cuda_time": 0.04249982092287531, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.855, + "pct_cuda_time": 0.04249982092287531, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 466.367, + "cuda_time_us": 519.704, + "pct_cuda_time": 2.2412305360631137, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.627, + "cuda_time_us": 323.323, + "pct_cuda_time": 1.394334814839859, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.587, + "pct_cuda_time": 1.3911608048754516, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 108.708, + "cuda_time_us": 46.047, + "pct_cuda_time": 0.19857831091178477, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 46.047, + "pct_cuda_time": 0.19857831091178477, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.288, + "cuda_time_us": 150.334, + "pct_cuda_time": 0.6483174103114699, + "trace": "" + }, + "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": 150.334, + "pct_cuda_time": 0.6483174103114699, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2435.96, + "cuda_time_us": 707.894, + "pct_cuda_time": 3.052802458891719, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.014, + "cuda_time_us": 10.048, + "pct_cuda_time": 0.043332136035824566, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.048, + "pct_cuda_time": 0.043332136035824566, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1773.746, + "cuda_time_us": 169.982, + "pct_cuda_time": 0.7330496763178276, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 186.083, + "cuda_time_us": 87.262, + "pct_cuda_time": 0.376318556405068, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.003169697450868934, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.527, + "pct_cuda_time": 0.3731488589541991, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 520.048, + "cuda_time_us": 12.832, + "pct_cuda_time": 0.055338173727279144, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 12.832, + "pct_cuda_time": 0.055338173727279144, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 735.345, + "cuda_time_us": 22.625, + "pct_cuda_time": 0.09757061881076143, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.728, + "pct_cuda_time": 0.024702077549084704, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.424, + "pct_cuda_time": 0.06651620881932306, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.473, + "pct_cuda_time": 0.006352332442353661, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 185.195, + "cuda_time_us": 47.263, + "pct_cuda_time": 0.20382232737471898, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.263, + "pct_cuda_time": 0.20382232737471898, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.783, + "cuda_time_us": 10.208, + "pct_cuda_time": 0.04402213820200011, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.208, + "pct_cuda_time": 0.04402213820200011, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 445.227, + "cuda_time_us": 517.656, + "pct_cuda_time": 2.2323985083360665, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.411, + "cuda_time_us": 322.811, + "pct_cuda_time": 1.3921268079080973, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.075, + "pct_cuda_time": 1.3889527979436898, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.484, + "cuda_time_us": 45.12, + "pct_cuda_time": 0.1945806108615052, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.12, + "pct_cuda_time": 0.1945806108615052, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 139.191, + "cuda_time_us": 149.725, + "pct_cuda_time": 0.6456910895664641, + "trace": "" + }, + "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": 149.725, + "pct_cuda_time": 0.6456910895664641, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2561.004, + "cuda_time_us": 708.0859999999999, + "pct_cuda_time": 3.0536304614911294, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.903, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1889.427, + "cuda_time_us": 170.302, + "pct_cuda_time": 0.7344296806501786, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.605, + "cuda_time_us": 86.879, + "pct_cuda_time": 0.3746668637197853, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.143, + "pct_cuda_time": 0.3714928537553777, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 475.059, + "cuda_time_us": 13.28, + "pct_cuda_time": 0.05727017979257067, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.28, + "pct_cuda_time": 0.05727017979257067, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 951.831, + "cuda_time_us": 22.752, + "pct_cuda_time": 0.09811830803016326, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.888, + "pct_cuda_time": 0.025392079715260252, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.424, + "pct_cuda_time": 0.06651620881932306, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.44, + "pct_cuda_time": 0.006210019495579953, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 182.655, + "cuda_time_us": 47.391, + "pct_cuda_time": 0.2043743291076594, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.391, + "pct_cuda_time": 0.2043743291076594, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.746, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.04319413560258945, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 454.308, + "cuda_time_us": 517.752, + "pct_cuda_time": 2.2328125096357723, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.94, + "cuda_time_us": 322.93899999999996, + "pct_cuda_time": 1.3926788096410376, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.203, + "pct_cuda_time": 1.3895047996766303, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.745, + "cuda_time_us": 45.599, + "pct_cuda_time": 0.19664630484649326, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.599, + "pct_cuda_time": 0.19664630484649326, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.83, + "cuda_time_us": 149.214, + "pct_cuda_time": 0.643487395148241, + "trace": "" + }, + "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": 149.214, + "pct_cuda_time": 0.643487395148241, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2479.306, + "cuda_time_us": 709.4929999999999, + "pct_cuda_time": 3.0596981680399358, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.265, + "cuda_time_us": 10.175, + "pct_cuda_time": 0.043879825255226414, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.175, + "pct_cuda_time": 0.043879825255226414, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1790.39, + "cuda_time_us": 170.078, + "pct_cuda_time": 0.7334636776175328, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.209, + "cuda_time_us": 87.58200000000001, + "pct_cuda_time": 0.3776985607374191, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.846, + "pct_cuda_time": 0.37452455077301156, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 549.961, + "cuda_time_us": 12.799, + "pct_cuda_time": 0.05519586078050543, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 12.799, + "pct_cuda_time": 0.05519586078050543, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 746.014, + "cuda_time_us": 22.560999999999996, + "pct_cuda_time": 0.09729461794429119, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.728, + "pct_cuda_time": 0.024702077549084704, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.52, + "pct_cuda_time": 0.06693021011902839, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.00566233027617811, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 198.25, + "cuda_time_us": 47.136, + "pct_cuda_time": 0.20327463815531716, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.136, + "pct_cuda_time": 0.20327463815531716, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 90.05, + "cuda_time_us": 9.792, + "pct_cuda_time": 0.042228132569943684, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.792, + "pct_cuda_time": 0.042228132569943684, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 466.843, + "cuda_time_us": 519.448, + "pct_cuda_time": 2.240126532597233, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 170.675, + "cuda_time_us": 323.291, + "pct_cuda_time": 1.3941968144066241, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.555, + "pct_cuda_time": 1.3910228044422166, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.853, + "cuda_time_us": 46.335, + "pct_cuda_time": 0.19982031481090076, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 46.335, + "pct_cuda_time": 0.19982031481090076, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.698, + "cuda_time_us": 149.822, + "pct_cuda_time": 0.6461094033797082, + "trace": "" + }, + "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": 149.822, + "pct_cuda_time": 0.6461094033797082, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2399.01, + "cuda_time_us": 708.759, + "pct_cuda_time": 3.0565327831026057, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.391, + "cuda_time_us": 10.368, + "pct_cuda_time": 0.04471214036817567, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.368, + "pct_cuda_time": 0.04471214036817567, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1726.997, + "cuda_time_us": 170.175, + "pct_cuda_time": 0.7338819914307768, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 158.792, + "cuda_time_us": 87.007, + "pct_cuda_time": 0.3752188654527257, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.271, + "pct_cuda_time": 0.37204485548831817, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 487.234, + "cuda_time_us": 13.344, + "pct_cuda_time": 0.0575461806590409, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.344, + "pct_cuda_time": 0.0575461806590409, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 746.459, + "cuda_time_us": 22.592000000000002, + "pct_cuda_time": 0.09742830586398772, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.728, + "pct_cuda_time": 0.024702077549084704, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.488, + "pct_cuda_time": 0.06679220968579327, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.005934018629109732, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 181.819, + "cuda_time_us": 47.232, + "pct_cuda_time": 0.2036886394550225, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.232, + "pct_cuda_time": 0.2036886394550225, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.562, + "cuda_time_us": 10.239, + "pct_cuda_time": 0.04415582612169663, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.239, + "pct_cuda_time": 0.04415582612169663, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 455.566, + "cuda_time_us": 517.977, + "pct_cuda_time": 2.2337828251819567, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 161.401, + "cuda_time_us": 323.22799999999995, + "pct_cuda_time": 1.3939251260536922, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0033120103976426417, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.46, + "pct_cuda_time": 1.3906131156560497, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.786, + "cuda_time_us": 45.439, + "pct_cuda_time": 0.1959563026803177, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.439, + "pct_cuda_time": 0.1959563026803177, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.494, + "cuda_time_us": 149.31, + "pct_cuda_time": 0.6439013964479464, + "trace": "" + }, + "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": 149.31, + "pct_cuda_time": 0.6439013964479464, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2367.826, + "cuda_time_us": 707.4169999999999, + "pct_cuda_time": 3.050745389933808, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.726, + "cuda_time_us": 9.76, + "pct_cuda_time": 0.04209013213670857, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.76, + "pct_cuda_time": 0.04209013213670857, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1689.672, + "cuda_time_us": 169.567, + "pct_cuda_time": 0.7312599831993097, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.086, + "cuda_time_us": 86.495, + "pct_cuda_time": 0.37301085852096394, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 85.759, + "pct_cuda_time": 0.3698368485565564, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 460.583, + "cuda_time_us": 12.768, + "pct_cuda_time": 0.05506217286080892, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 12.768, + "pct_cuda_time": 0.05506217286080892, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 772.602, + "cuda_time_us": 22.720000000000002, + "pct_cuda_time": 0.09798030759692816, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.696, + "pct_cuda_time": 0.024564077115849594, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.52, + "pct_cuda_time": 0.06693021011902839, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.006486020362050174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 171.866, + "cuda_time_us": 47.584, + "pct_cuda_time": 0.2052066442206087, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.584, + "pct_cuda_time": 0.2052066442206087, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.594, + "cuda_time_us": 10.049, + "pct_cuda_time": 0.04333644854936316, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.049, + "pct_cuda_time": 0.04333644854936316, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 473.159, + "cuda_time_us": 518.0409999999999, + "pct_cuda_time": 2.234058826048426, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 161.469, + "cuda_time_us": 323.196, + "pct_cuda_time": 1.3937871256204575, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.704, + "pct_cuda_time": 0.0030360095311724217, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.492, + "pct_cuda_time": 1.390751116089285, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 114.047, + "cuda_time_us": 45.535, + "pct_cuda_time": 0.19637030398002303, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.535, + "pct_cuda_time": 0.19637030398002303, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.716, + "cuda_time_us": 149.31, + "pct_cuda_time": 0.6439013964479464, + "trace": "" + }, + "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": 149.31, + "pct_cuda_time": 0.6439013964479464, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2361.896, + "cuda_time_us": 707.415, + "pct_cuda_time": 3.0507367649067305, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.198, + "cuda_time_us": 9.76, + "pct_cuda_time": 0.04209013213670857, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.76, + "pct_cuda_time": 0.04209013213670857, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1701.238, + "cuda_time_us": 171.00600000000003, + "pct_cuda_time": 0.7374656901813511, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 158.753, + "cuda_time_us": 87.74300000000001, + "pct_cuda_time": 0.3783928754171333, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 87.007, + "pct_cuda_time": 0.3752188654527257, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 494.353, + "cuda_time_us": 12.896, + "pct_cuda_time": 0.055614174593749365, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 12.896, + "pct_cuda_time": 0.055614174593749365, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 731.119, + "cuda_time_us": 22.816, + "pct_cuda_time": 0.09839430889663349, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.76, + "pct_cuda_time": 0.02484007798231981, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.488, + "pct_cuda_time": 0.06679220968579327, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.568, + "pct_cuda_time": 0.006762021228520394, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 175.119, + "cuda_time_us": 47.551, + "pct_cuda_time": 0.205064331273835, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.551, + "pct_cuda_time": 0.205064331273835, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.078, + "cuda_time_us": 9.664, + "pct_cuda_time": 0.041676130837003236, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.664, + "pct_cuda_time": 0.041676130837003236, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 444.008, + "cuda_time_us": 516.9849999999999, + "pct_cuda_time": 2.229504811751668, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.498, + "cuda_time_us": 322.811, + "pct_cuda_time": 1.3921268079080973, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.075, + "pct_cuda_time": 1.3889527979436898, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.383, + "cuda_time_us": 45.152, + "pct_cuda_time": 0.19471861129474033, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.152, + "pct_cuda_time": 0.19471861129474033, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 137.508, + "cuda_time_us": 149.022, + "pct_cuda_time": 0.6426593925488303, + "trace": "" + }, + "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": 149.022, + "pct_cuda_time": 0.6426593925488303, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2357.609, + "cuda_time_us": 709.012, + "pct_cuda_time": 3.0576238490278707, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.806, + "cuda_time_us": 9.695, + "pct_cuda_time": 0.04180981875669976, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.695, + "pct_cuda_time": 0.04180981875669976, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1651.382, + "cuda_time_us": 170.109, + "pct_cuda_time": 0.7335973655372294, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.556, + "cuda_time_us": 87.29400000000001, + "pct_cuda_time": 0.37645655683830315, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 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": 86.558, + "pct_cuda_time": 0.3732825468738956, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1024, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 470.187, + "cuda_time_us": 13.088, + "pct_cuda_time": 0.05644217719316002, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 13.088, + "pct_cuda_time": 0.05644217719316002, + "trace": "_C::rotary_embedding(int64[1024], bfloat16[1024, 4096], bfloat16[1024, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 728.779, + "cuda_time_us": 22.527, + "pct_cuda_time": 0.0971479924839789, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 5.888, + "pct_cuda_time": 0.025392079715260252, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1024], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.359, + "pct_cuda_time": 0.06623589543931424, + "trace": "_vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.005520017329404403, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], None, None, bfloat16[1024, 32, 128], int32[9], int32[9], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1024, 32, 128], bfloat16[1024, 8, 128], bfloat16[1024, 8, 128], bfloat16[1024, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 172.7, + "cuda_time_us": 47.2, + "pct_cuda_time": 0.20355063902178736, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 47.2, + "pct_cuda_time": 0.20355063902178736, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1024, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.359, + "cuda_time_us": 10.175, + "pct_cuda_time": 0.043879825255226414, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.175, + "pct_cuda_time": 0.043879825255226414, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 480.394, + "cuda_time_us": 519.0329999999999, + "pct_cuda_time": 2.238336839478715, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 154.218, + "cuda_time_us": 323.51599999999996, + "pct_cuda_time": 1.3951671299528083, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 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": 322.78, + "pct_cuda_time": 1.3919931199884008, + "trace": "mm(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1024, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1024, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 135.192, + "cuda_time_us": 45.919, + "pct_cuda_time": 0.19802630917884434, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 45.919, + "pct_cuda_time": 0.19802630917884434, + "trace": "_C::silu_and_mul(bfloat16[1024, 14336], bfloat16[1024, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 139.787, + "cuda_time_us": 149.598, + "pct_cuda_time": 0.6451434003470624, + "trace": "" + }, + "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": 149.598, + "pct_cuda_time": 0.6451434003470624, + "trace": "mm(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1024, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1024, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.851, + "cuda_time_us": 10.208, + "pct_cuda_time": 0.04402213820200011, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 10.208, + "pct_cuda_time": 0.04402213820200011, + "trace": "_C::fused_add_rms_norm(bfloat16[1024, 4096], bfloat16[1024, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 442.803, + "cuda_time_us": 363.13100000000003, + "pct_cuda_time": 1.5660073537843364, + "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": 5.6, + "pct_cuda_time": 0.02415007581614426, + "trace": "index_select(bfloat16[1024, 4096], 0, int64[8])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0031740099644075315, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[8, 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": 356.795, + "pct_cuda_time": 1.5386832680037843, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[8, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 3666.118, + "cuda_time_us": 120.00000000000001, + "pct_cuda_time": 0.5175016246316628, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.704, + "pct_cuda_time": 0.0030360095311724217, + "trace": "copy_(bfloat16[8], bfloat16[8], True) <- _to_copy(bfloat16[8], 15, 0, None, None, True, None) <- to(bfloat16[8], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.003169697450868934, + "trace": "copy_(bfloat16[8], bfloat16[8], True) <- _to_copy(bfloat16[8], 15, 0, None, None, True, None) <- to(bfloat16[8], 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": 0.003450010830877752, + "trace": "copy_(int32[8], int32[8], True) <- _to_copy(int32[8], 3, 0, None, None, True, None) <- to(int32[8], 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": 0.0033120103976426417, + "trace": "copy_(bfloat16[8], bfloat16[8], True) <- _to_copy(bfloat16[8], 15, 0, None, None, True, None) <- to(bfloat16[8], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.801, + "pct_cuda_time": 0.003454323344416349, + "trace": "copy_(bfloat16[8], bfloat16[8], True) <- _to_copy(bfloat16[8], 15, 0, None, None, True, None) <- to(bfloat16[8], 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": 0.003450010830877752, + "trace": "copy_(bfloat16[8], bfloat16[8], True) <- _to_copy(bfloat16[8], 15, 0, None, None, True, None) <- to(bfloat16[8], 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": 0.003450010830877752, + "trace": "copy_(bfloat16[8], bfloat16[8], True) <- _to_copy(bfloat16[8], 15, 0, None, None, True, None) <- to(bfloat16[8], 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": 4.736, + "pct_cuda_time": 0.02042406411879629, + "trace": "copy_(float32[8, 128256], bfloat16[8, 128256], False) <- _to_copy(bfloat16[8, 128256], 6, None, None, None, False, None) <- to(bfloat16[8, 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": 6.432, + "pct_cuda_time": 0.027738087080257125, + "trace": "div_(float32[8, 128256], bfloat16[8, 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": 35.2, + "pct_cuda_time": 0.15180047655862108, + "trace": "_softmax(float32[8, 128256], -1, False) <- softmax(float32[8, 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": 28.128, + "pct_cuda_time": 0.12130238081366175, + "trace": "_log_softmax(float32[8, 128256], -1, False) <- log_softmax(float32[8, 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": 1.92, + "pct_cuda_time": 0.008280025994106604, + "trace": "copy_(int64[8], int32[8], False) <- _to_copy(int32[8], 4, None, None, None, False, None) <- to(int32[8], 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": 7.296, + "pct_cuda_time": 0.0314640987776051, + "trace": "index(float32[8, 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": 0.11950837518160531, + "trace": "argmax(float32[8, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.013662042890275899, + "trace": "copy_(int64[8], int64[8], False) <- _to_copy(int64[8], 4, 0, None, None, False, None) <- to(int64[8], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + }, + "decode_1": { + "metadata": { + "num_running_seqs": 8 + }, + "summary_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cuda_time_us": 6367.842000000001, + "pct_cuda_time": 93.14685167385447, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cuda_time_us": 7.488, + "pct_cuda_time": 0.10953218144134579, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cuda_time_us": 7.488, + "pct_cuda_time": 0.10953218144134579, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cuda_time_us": 6357.346, + "pct_cuda_time": 92.99331938533838, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cuda_time_us": 199.90099999999998, + "pct_cuda_time": 2.924090892402038, + "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": 0.06085121191185877, + "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": 195.74099999999999, + "pct_cuda_time": 2.8632396804901794, + "invocations": 63 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cuda_time_us": 1850.8499999999997, + "pct_cuda_time": 27.073669607467256, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cuda_time_us": 678.7440000000001, + "pct_cuda_time": 9.928460331226605, + "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": 678.7440000000001, + "pct_cuda_time": 9.928460331226605, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cuda_time_us": 119.93200000000004, + "pct_cuda_time": 1.754328737262752, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cuda_time_us": 119.93200000000004, + "pct_cuda_time": 1.754328737262752, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cuda_time_us": 482.93399999999997, + "pct_cuda_time": 7.064211339769616, + "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": 81.15, + "pct_cuda_time": 1.1870374631363798, + "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": 359.7039999999999, + "pct_cuda_time": 5.26164046383251, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cuda_time_us": 42.080000000000005, + "pct_cuda_time": 0.6155334128007254, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cuda_time_us": 569.2400000000001, + "pct_cuda_time": 8.326669199208292, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cuda_time_us": 501.01599999999996, + "pct_cuda_time": 7.328709323853805, + "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": 68.224, + "pct_cuda_time": 0.9979598753544838, + "invocations": 32 + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cuda_time_us": 4306.594999999999, + "pct_cuda_time": 62.99555888546908, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cuda_time_us": 2620.2509999999993, + "pct_cuda_time": 38.32823289982207, + "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": 2620.2509999999993, + "pct_cuda_time": 38.32823289982207, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cuda_time_us": 291.67400000000004, + "pct_cuda_time": 4.266518361341225, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cuda_time_us": 291.67400000000004, + "pct_cuda_time": 4.266518361341225, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cuda_time_us": 1394.6699999999998, + "pct_cuda_time": 20.400807624305784, + "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": 1394.6699999999998, + "pct_cuda_time": 20.400807624305784, + "invocations": 32 + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04400010707472865, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04400010707472865, + "invocations": 1 + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cuda_time_us": 349.083, + "pct_cuda_time": 5.106279713420047, + "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": 5.6, + "pct_cuda_time": 0.08191509295827142, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cuda_time_us": 0.736, + "pct_cuda_time": 0.010765983645944244, + "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": 342.747, + "pct_cuda_time": 5.013598636815832, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cuda_time_us": 119.422, + "pct_cuda_time": 1.7468686127254802, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cuda_time_us": 5.374999999999999, + "pct_cuda_time": 0.07862386154476943, + "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": 4.833, + "pct_cuda_time": 0.07069565076202246, + "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": 6.4, + "pct_cuda_time": 0.09361724909516735, + "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": 34.976, + "pct_cuda_time": 0.5116182663050894, + "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": 28.351, + "pct_cuda_time": 0.4147097857964202, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cuda_time_us": 2.016, + "pct_cuda_time": 0.02948943346497771, + "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": 7.392, + "pct_cuda_time": 0.10812792270491828, + "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": 27.615, + "pct_cuda_time": 0.40394380215047593, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cuda_time_us": 2.464, + "pct_cuda_time": 0.036042640901639424, + "invocations": 1 + }, + "children": [] + } + ] + } + ], + "model_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cpu_time_us": 84812.452, + "cuda_time_us": 6367.842000000001, + "pct_cuda_time": 93.14685167385447, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cpu_time_us": 365.763, + "cuda_time_us": 7.488, + "pct_cuda_time": 0.10953218144134579, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 7.488, + "pct_cuda_time": 0.10953218144134579, + "trace": "index_select(bfloat16[128256, 4096], 0, int64[8]) <- embedding(bfloat16[128256, 4096], int64[8], -1, False, False)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 5171.138, + "cuda_time_us": 207.45299999999997, + "pct_cuda_time": 3.0345592463343354, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 356.146, + "cuda_time_us": 4.16, + "pct_cuda_time": 0.06085121191185877, + "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": 0.06085121191185877, + "trace": "_C::rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 3804.221, + "cuda_time_us": 64.127, + "pct_cuda_time": 0.9380302082384054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 823.026, + "cuda_time_us": 25.792, + "pct_cuda_time": 0.37727751385352437, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 25.792, + "pct_cuda_time": 0.37727751385352437, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 1054.458, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.0561703494571004, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.0561703494571004, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1235.124, + "cuda_time_us": 15.808, + "pct_cuda_time": 0.23123460526506331, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.4, + "pct_cuda_time": 0.03510646841068775, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.936, + "pct_cuda_time": 0.17459616956248708, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.021531967291888488, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 382.872, + "cuda_time_us": 18.686999999999998, + "pct_cuda_time": 0.27334773966271747, + "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": 16.575, + "pct_cuda_time": 0.2424540474613123, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 130.436, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04634053830210783, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04634053830210783, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 715.324, + "cuda_time_us": 135.998, + "pct_cuda_time": 1.9893372878819635, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 300.09, + "cuda_time_us": 83.615, + "pct_cuda_time": 1.2230947317331902, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 83.615, + "pct_cuda_time": 1.2230947317331902, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 161.096, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.1310641487332343, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1310641487332343, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 176.02, + "cuda_time_us": 43.423, + "pct_cuda_time": 0.6351784074155393, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.423, + "pct_cuda_time": 0.6351784074155393, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2693.506, + "cuda_time_us": 201.887, + "pct_cuda_time": 2.9531414950118826, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.301, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04400010707472865, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04400010707472865, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1912.746, + "cuda_time_us": 57.248, + "pct_cuda_time": 0.8374062931562718, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.92, + "cuda_time_us": 20.383, + "pct_cuda_time": 0.29815631067293685, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.383, + "pct_cuda_time": 0.29815631067293685, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 550.734, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.055234176966148726, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.055234176966148726, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 842.796, + "cuda_time_us": 15.137, + "pct_cuda_time": 0.22141942180524185, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.56, + "pct_cuda_time": 0.03744689963806694, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.201, + "pct_cuda_time": 0.16384481361171396, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.020127708555460977, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 183.755, + "cuda_time_us": 17.951999999999998, + "pct_cuda_time": 0.2625963837119443, + "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": 0.23123460526506331, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.03136177844688106, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.771, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.045872452056632, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045872452056632, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 510.354, + "cuda_time_us": 138.495, + "pct_cuda_time": 2.02586264272425, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 185.396, + "cuda_time_us": 84.799, + "pct_cuda_time": 1.2404139228157962, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 84.799, + "pct_cuda_time": 1.2404139228157962, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 106.497, + "cuda_time_us": 9.088, + "pct_cuda_time": 0.1329364937151376, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1329364937151376, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.717, + "cuda_time_us": 44.608, + "pct_cuda_time": 0.6525122261933163, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6525122261933163, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2540.4, + "cuda_time_us": 200.41299999999998, + "pct_cuda_time": 2.9315802723296516, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 107.781, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044936279565680325, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044936279565680325, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1743.582, + "cuda_time_us": 57.536, + "pct_cuda_time": 0.8416190693655543, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.12, + "cuda_time_us": 20.896, + "pct_cuda_time": 0.3056603182957214, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.896, + "pct_cuda_time": 0.3056603182957214, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 535.789, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.05476609072067289, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05476609072067289, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 731.141, + "cuda_time_us": 14.943999999999999, + "pct_cuda_time": 0.21859627663721573, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.036510727147115264, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.168, + "pct_cuda_time": 0.163362099671067, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 164.573, + "cuda_time_us": 17.952, + "pct_cuda_time": 0.2625963837119444, + "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": 0.23168806381536805, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030908319896576338, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.05, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.045872452056632, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045872452056632, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 482.644, + "cuda_time_us": 136.66899999999998, + "pct_cuda_time": 1.999152471341785, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 168.499, + "cuda_time_us": 83.838, + "pct_cuda_time": 1.2263567077563498, + "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": 83.838, + "pct_cuda_time": 1.2263567077563498, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 109.736, + "cuda_time_us": 9.216, + "pct_cuda_time": 0.13480883869704097, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13480883869704097, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 143.592, + "cuda_time_us": 43.615, + "pct_cuda_time": 0.6379869248883943, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6379869248883943, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2459.178, + "cuda_time_us": 198.779, + "pct_cuda_time": 2.907678618420042, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.172, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04446819332020449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04446819332020449, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1753.941, + "cuda_time_us": 57.758, + "pct_cuda_time": 0.844866417693543, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 141.601, + "cuda_time_us": 21.28, + "pct_cuda_time": 0.3112773532414314, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.28, + "pct_cuda_time": 0.3112773532414314, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 518.29, + "cuda_time_us": 3.615, + "pct_cuda_time": 0.05287911804359843, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.615, + "pct_cuda_time": 0.05287911804359843, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 766.693, + "cuda_time_us": 15.039, + "pct_cuda_time": 0.21998590767847212, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.036510727147115264, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.231, + "pct_cuda_time": 0.16428364446684757, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.019191536064509306, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 168.743, + "cuda_time_us": 17.823999999999998, + "pct_cuda_time": 0.26072403873004096, + "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": 0.22983034652863582, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.916, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04538973811598504, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04538973811598504, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.195, + "cuda_time_us": 134.878, + "pct_cuda_time": 1.9729542692903093, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 168.281, + "cuda_time_us": 82.175, + "pct_cuda_time": 1.2020308506867774, + "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": 82.175, + "pct_cuda_time": 1.2020308506867774, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.345, + "cuda_time_us": 9.28, + "pct_cuda_time": 0.13574501118799265, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.28, + "pct_cuda_time": 0.13574501118799265, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.06, + "cuda_time_us": 43.423, + "pct_cuda_time": 0.6351784074155393, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.423, + "pct_cuda_time": 0.6351784074155393, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2422.505, + "cuda_time_us": 199.421, + "pct_cuda_time": 2.9170695987199005, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.918, + "cuda_time_us": 3.071, + "pct_cuda_time": 0.044921651870509206, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.071, + "pct_cuda_time": 0.044921651870509206, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1694.108, + "cuda_time_us": 58.688, + "pct_cuda_time": 0.8584701742026845, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 143.651, + "cuda_time_us": 22.24, + "pct_cuda_time": 0.32531994060570646, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.24, + "pct_cuda_time": 0.32531994060570646, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 477.339, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.0561703494571004, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.0561703494571004, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 739.174, + "cuda_time_us": 14.943999999999999, + "pct_cuda_time": 0.21859627663721573, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.036510727147115264, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.168, + "pct_cuda_time": 0.163362099671067, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 167.734, + "cuda_time_us": 17.664, + "pct_cuda_time": 0.25838360750266187, + "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": 0.22748991530125662, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.053, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.045872452056632, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045872452056632, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 498.156, + "cuda_time_us": 134.526, + "pct_cuda_time": 1.9678053205900752, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 174.4, + "cuda_time_us": 81.822, + "pct_cuda_time": 1.1968672742913722, + "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": 81.822, + "pct_cuda_time": 1.1968672742913722, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.219, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.13434075245156513, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13434075245156513, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 147.867, + "cuda_time_us": 43.52, + "pct_cuda_time": 0.6365972938471379, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6365972938471379, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2640.671, + "cuda_time_us": 197.53300000000002, + "pct_cuda_time": 2.889452510236827, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.178, + "cuda_time_us": 3.041, + "pct_cuda_time": 0.0444828210153756, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0444828210153756, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1907.797, + "cuda_time_us": 57.086, + "pct_cuda_time": 0.8350366065385504, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.532, + "cuda_time_us": 20.416, + "pct_cuda_time": 0.2986390246135838, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.2986390246135838, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 542.477, + "cuda_time_us": 3.935, + "pct_cuda_time": 0.0575599804983568, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.935, + "pct_cuda_time": 0.0575599804983568, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 878.984, + "cuda_time_us": 15.103, + "pct_cuda_time": 0.2209220801694238, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.0369788133925911, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.231, + "pct_cuda_time": 0.16428364446684757, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.019659622309985143, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 184.829, + "cuda_time_us": 17.631999999999998, + "pct_cuda_time": 0.257915521257186, + "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": 0.22702182905578078, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.276, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.052893745738769544, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.052893745738769544, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 490.224, + "cuda_time_us": 133.79000000000002, + "pct_cuda_time": 1.9570393369441312, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 176.317, + "cuda_time_us": 80.799, + "pct_cuda_time": 1.1819031421313166, + "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": 80.799, + "pct_cuda_time": 1.1819031421313166, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 111.061, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.13153223497871014, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13153223497871014, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 150.89, + "cuda_time_us": 43.999, + "pct_cuda_time": 0.6436039598341043, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.999, + "pct_cuda_time": 0.6436039598341043, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2418.716, + "cuda_time_us": 198.78, + "pct_cuda_time": 2.907693246115213, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.24, + "cuda_time_us": 3.071, + "pct_cuda_time": 0.044921651870509206, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.071, + "pct_cuda_time": 0.044921651870509206, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1685.213, + "cuda_time_us": 58.271, + "pct_cuda_time": 0.8523704253163276, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 138.284, + "cuda_time_us": 21.472, + "pct_cuda_time": 0.31408587071428645, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.472, + "pct_cuda_time": 0.31408587071428645, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 494.757, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.055234176966148726, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.055234176966148726, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 732.483, + "cuda_time_us": 14.943999999999999, + "pct_cuda_time": 0.21859627663721573, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.036510727147115264, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.168, + "pct_cuda_time": 0.163362099671067, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 158.528, + "cuda_time_us": 18.079, + "pct_cuda_time": 0.2644541009986766, + "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": 15.967, + "pct_cuda_time": 0.2335604087972714, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 108.193, + "cuda_time_us": 3.264, + "pct_cuda_time": 0.04774479703853534, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04774479703853534, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 477.454, + "cuda_time_us": 134.174, + "pct_cuda_time": 1.9626563718898409, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 172.089, + "cuda_time_us": 82.111, + "pct_cuda_time": 1.2010946781958258, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 82.111, + "pct_cuda_time": 1.2010946781958258, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.718, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.13200032122418592, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13200032122418592, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 150.744, + "cuda_time_us": 43.039, + "pct_cuda_time": 0.6295613724698292, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.039, + "pct_cuda_time": 0.6295613724698292, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2420.477, + "cuda_time_us": 198.014, + "pct_cuda_time": 2.8964884316141353, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.607, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04680862454758367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04680862454758367, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1723.333, + "cuda_time_us": 57.248, + "pct_cuda_time": 0.8374062931562718, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.396, + "cuda_time_us": 20.608, + "pct_cuda_time": 0.30144754208643887, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.30144754208643887, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 505.25, + "cuda_time_us": 3.775, + "pct_cuda_time": 0.05521954927097761, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05521954927097761, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 748.862, + "cuda_time_us": 15.169, + "pct_cuda_time": 0.22188750805071772, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03838307212901861, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.232, + "pct_cuda_time": 0.16429827216201867, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01920616375968042, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 167.488, + "cuda_time_us": 17.695999999999998, + "pct_cuda_time": 0.25885169374813766, + "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": 0.22748991530125662, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.03136177844688106, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.962, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044936279565680325, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044936279565680325, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 475.182, + "cuda_time_us": 134.494, + "pct_cuda_time": 1.9673372343445992, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 162.208, + "cuda_time_us": 81.567, + "pct_cuda_time": 1.1931372120227366, + "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": 81.567, + "pct_cuda_time": 1.1931372120227366, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.813, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.13200032122418592, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13200032122418592, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.191, + "cuda_time_us": 43.903, + "pct_cuda_time": 0.6421997010976768, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6421997010976768, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2373.38, + "cuda_time_us": 199.291, + "pct_cuda_time": 2.9151679983476555, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.772, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044936279565680325, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044936279565680325, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1685.185, + "cuda_time_us": 58.717, + "pct_cuda_time": 0.858894377362647, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 141.083, + "cuda_time_us": 21.567, + "pct_cuda_time": 0.3154755017555428, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.567, + "pct_cuda_time": 0.3154755017555428, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 478.563, + "cuda_time_us": 3.872, + "pct_cuda_time": 0.056638435702576234, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.056638435702576234, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 745.0, + "cuda_time_us": 15.168, + "pct_cuda_time": 0.2218728803555466, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03978733086544612, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.168, + "pct_cuda_time": 0.163362099671067, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 158.672, + "cuda_time_us": 18.11, + "pct_cuda_time": 0.2649075595489813, + "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": 0.23075189132441637, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 2.335, + "pct_cuda_time": 0.03415566822456496, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.402, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.045872452056632, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045872452056632, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 467.111, + "cuda_time_us": 134.366, + "pct_cuda_time": 1.965464889362696, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.976, + "cuda_time_us": 82.143, + "pct_cuda_time": 1.2015627644413016, + "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": 82.143, + "pct_cuda_time": 1.2015627644413016, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.256, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.13340457996061345, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.13340457996061345, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 152.234, + "cuda_time_us": 43.103, + "pct_cuda_time": 0.6304975449607809, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.103, + "pct_cuda_time": 0.6304975449607809, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2589.826, + "cuda_time_us": 196.603, + "pct_cuda_time": 2.875848753727685, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.353, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04538973811598504, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04538973811598504, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1826.0, + "cuda_time_us": 57.150999999999996, + "pct_cuda_time": 0.8359874067246732, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 230.473, + "cuda_time_us": 20.672, + "pct_cuda_time": 0.30238371457739055, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.30238371457739055, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 523.983, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.055234176966148726, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.055234176966148726, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 742.234, + "cuda_time_us": 15.007, + "pct_cuda_time": 0.2195178214329963, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.495, + "pct_cuda_time": 0.03649609945194414, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.232, + "pct_cuda_time": 0.16429827216201867, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 166.219, + "cuda_time_us": 17.695999999999998, + "pct_cuda_time": 0.25885169374813766, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.584, + "pct_cuda_time": 0.22795800154673246, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.087, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04400010707472865, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04400010707472865, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 531.167, + "cuda_time_us": 133.341, + "pct_cuda_time": 1.9504715018122982, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.656, + "cuda_time_us": 80.959, + "pct_cuda_time": 1.1842435733586958, + "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": 1.1842435733586958, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.197, + "cuda_time_us": 9.119, + "pct_cuda_time": 0.13338995226544234, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.119, + "pct_cuda_time": 0.13338995226544234, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 208.939, + "cuda_time_us": 43.263, + "pct_cuda_time": 0.6328379761881601, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6328379761881601, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2432.062, + "cuda_time_us": 199.839, + "pct_cuda_time": 2.923183975301429, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.642, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04446819332020449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04446819332020449, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1703.592, + "cuda_time_us": 58.464, + "pct_cuda_time": 0.8551935704843536, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.876, + "cuda_time_us": 22.112, + "pct_cuda_time": 0.3234475956238031, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.112, + "pct_cuda_time": 0.3234475956238031, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 479.507, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05382991822972122, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05382991822972122, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 748.669, + "cuda_time_us": 15.008, + "pct_cuda_time": 0.2195324491281674, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.56, + "pct_cuda_time": 0.03744689963806694, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.168, + "pct_cuda_time": 0.163362099671067, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 153.965, + "cuda_time_us": 17.664, + "pct_cuda_time": 0.25838360750266187, + "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": 0.22748991530125662, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.378, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.045872452056632, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045872452056632, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 505.406, + "cuda_time_us": 135.199, + "pct_cuda_time": 1.977649759440239, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 172.57, + "cuda_time_us": 82.336, + "pct_cuda_time": 1.204385909609328, + "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": 82.336, + "pct_cuda_time": 1.204385909609328, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.037, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.13153223497871014, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13153223497871014, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 163.195, + "cuda_time_us": 43.871, + "pct_cuda_time": 0.641731614852201, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.871, + "pct_cuda_time": 0.641731614852201, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2595.509, + "cuda_time_us": 197.88500000000002, + "pct_cuda_time": 2.894601458937061, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.279, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04400010707472865, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04400010707472865, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1896.811, + "cuda_time_us": 56.96000000000001, + "pct_cuda_time": 0.8331935169469893, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.078, + "cuda_time_us": 20.545, + "pct_cuda_time": 0.3005259972906583, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.545, + "pct_cuda_time": 0.3005259972906583, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 505.086, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053361831984245385, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053361831984245385, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 820.755, + "cuda_time_us": 15.04, + "pct_cuda_time": 0.22000053537364322, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.0369788133925911, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.232, + "pct_cuda_time": 0.16429827216201867, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 172.356, + "cuda_time_us": 17.727, + "pct_cuda_time": 0.2593051522984424, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.584, + "pct_cuda_time": 0.22795800154673246, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.143, + "pct_cuda_time": 0.031347150751709935, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.694, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044936279565680325, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044936279565680325, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 483.058, + "cuda_time_us": 134.845, + "pct_cuda_time": 1.9724715553496623, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 173.223, + "cuda_time_us": 82.111, + "pct_cuda_time": 1.2010946781958258, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 82.111, + "pct_cuda_time": 1.2010946781958258, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 114.433, + "cuda_time_us": 9.151, + "pct_cuda_time": 0.13385803851091818, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.151, + "pct_cuda_time": 0.13385803851091818, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 143.812, + "cuda_time_us": 43.583, + "pct_cuda_time": 0.6375188386429185, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.583, + "pct_cuda_time": 0.6375188386429185, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2621.696, + "cuda_time_us": 198.878, + "pct_cuda_time": 2.909126760241983, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.486, + "cuda_time_us": 3.073, + "pct_cuda_time": 0.04495090726085144, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.073, + "pct_cuda_time": 0.04495090726085144, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1902.508, + "cuda_time_us": 58.686, + "pct_cuda_time": 0.8584409188123423, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 140.298, + "cuda_time_us": 22.208, + "pct_cuda_time": 0.3248518543602307, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.208, + "pct_cuda_time": 0.3248518543602307, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 479.697, + "cuda_time_us": 3.775, + "pct_cuda_time": 0.05521954927097761, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05521954927097761, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 946.886, + "cuda_time_us": 15.039, + "pct_cuda_time": 0.21998590767847212, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03838307212901861, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.135, + "pct_cuda_time": 0.16287938573042005, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 173.055, + "cuda_time_us": 17.664, + "pct_cuda_time": 0.25838360750266187, + "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": 0.22702182905578078, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.03136177844688106, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.85, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044936279565680325, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044936279565680325, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 484.525, + "cuda_time_us": 134.047, + "pct_cuda_time": 1.9607986546031086, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 177.966, + "cuda_time_us": 81.919, + "pct_cuda_time": 1.1982861607229707, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.919, + "pct_cuda_time": 1.1982861607229707, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.721, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.13434075245156513, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13434075245156513, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.125, + "cuda_time_us": 42.944, + "pct_cuda_time": 0.6281717414285729, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 42.944, + "pct_cuda_time": 0.6281717414285729, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2485.21, + "cuda_time_us": 197.31000000000003, + "pct_cuda_time": 2.886190534213667, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.545, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044936279565680325, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044936279565680325, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1745.469, + "cuda_time_us": 57.024, + "pct_cuda_time": 0.8341296894379411, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.512, + "cuda_time_us": 20.512, + "pct_cuda_time": 0.3000432833500113, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.3000432833500113, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 483.86, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05382991822972122, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05382991822972122, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 771.543, + "cuda_time_us": 15.136, + "pct_cuda_time": 0.22140479411007075, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03838307212901861, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.2, + "pct_cuda_time": 0.16383018591654283, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.019191536064509306, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 166.88, + "cuda_time_us": 17.695999999999998, + "pct_cuda_time": 0.25885169374813766, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.584, + "pct_cuda_time": 0.22795800154673246, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.361, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04400010707472865, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04400010707472865, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 497.002, + "cuda_time_us": 134.20600000000002, + "pct_cuda_time": 1.9631244581353169, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 170.212, + "cuda_time_us": 81.759, + "pct_cuda_time": 1.1959457294955915, + "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": 81.759, + "pct_cuda_time": 1.1959457294955915, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.218, + "cuda_time_us": 8.991, + "pct_cuda_time": 0.13151760728353898, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13151760728353898, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 161.575, + "cuda_time_us": 43.456, + "pct_cuda_time": 0.6356611213561862, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.456, + "pct_cuda_time": 0.6356611213561862, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2547.914, + "cuda_time_us": 198.94, + "pct_cuda_time": 2.910033677342592, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.039, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04540436581115616, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04540436581115616, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1835.944, + "cuda_time_us": 57.983, + "pct_cuda_time": 0.8481576491070449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 179.506, + "cuda_time_us": 21.279, + "pct_cuda_time": 0.3112627255462603, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.279, + "pct_cuda_time": 0.3112627255462603, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 517.639, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.0561703494571004, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.0561703494571004, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 810.244, + "cuda_time_us": 15.04, + "pct_cuda_time": 0.22000053537364322, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.036510727147115264, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.136, + "pct_cuda_time": 0.16289401342559115, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.020595794800936814, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 159.494, + "cuda_time_us": 17.823999999999998, + "pct_cuda_time": 0.26072403873004096, + "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": 0.22936226028315998, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.03136177844688106, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.871, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.047276710793059507, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047276710793059507, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 481.015, + "cuda_time_us": 134.62099999999998, + "pct_cuda_time": 1.9691949516313312, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 177.69, + "cuda_time_us": 82.014, + "pct_cuda_time": 1.1996757917642271, + "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": 82.014, + "pct_cuda_time": 1.1996757917642271, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.913, + "cuda_time_us": 9.408, + "pct_cuda_time": 0.13761735616989598, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.408, + "pct_cuda_time": 0.13761735616989598, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.519, + "cuda_time_us": 43.199, + "pct_cuda_time": 0.6319018036972084, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.199, + "pct_cuda_time": 0.6319018036972084, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2475.111, + "cuda_time_us": 196.79699999999997, + "pct_cuda_time": 2.8786865265908816, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.309, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04400010707472865, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04400010707472865, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1728.502, + "cuda_time_us": 56.830999999999996, + "pct_cuda_time": 0.8313065442699148, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.233, + "cuda_time_us": 20.511, + "pct_cuda_time": 0.3000286556548402, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.511, + "pct_cuda_time": 0.3000286556548402, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 494.886, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053361831984245385, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053361831984245385, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 766.059, + "cuda_time_us": 15.104, + "pct_cuda_time": 0.2209367078645949, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.036510727147115264, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.232, + "pct_cuda_time": 0.16429827216201867, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.020127708555460977, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 168.827, + "cuda_time_us": 17.567999999999998, + "pct_cuda_time": 0.2569793487662343, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.424, + "pct_cuda_time": 0.22561757031935328, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.03136177844688106, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.79, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04680862454758367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04680862454758367, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 507.437, + "cuda_time_us": 133.75799999999998, + "pct_cuda_time": 1.956571250698655, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 187.944, + "cuda_time_us": 81.279, + "pct_cuda_time": 1.1889244358134539, + "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": 1.1889244358134539, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.522, + "cuda_time_us": 8.991, + "pct_cuda_time": 0.13151760728353898, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13151760728353898, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.716, + "cuda_time_us": 43.488, + "pct_cuda_time": 0.636129207601662, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.488, + "pct_cuda_time": 0.636129207601662, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2553.231, + "cuda_time_us": 198.90999999999997, + "pct_cuda_time": 2.909594846487458, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.701, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04446819332020449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04446819332020449, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1839.781, + "cuda_time_us": 58.432, + "pct_cuda_time": 0.8547254842388777, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 157.13, + "cuda_time_us": 21.824, + "pct_cuda_time": 0.31923481941452064, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.824, + "pct_cuda_time": 0.31923481941452064, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 539.481, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05382991822972122, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05382991822972122, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 799.953, + "cuda_time_us": 15.231, + "pct_cuda_time": 0.22279442515132716, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.0369788133925911, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.36, + "pct_cuda_time": 0.16617061714392203, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.343, + "pct_cuda_time": 0.01964499461481402, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 169.973, + "cuda_time_us": 17.697, + "pct_cuda_time": 0.2588663214433088, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.584, + "pct_cuda_time": 0.22795800154673246, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030908319896576338, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.69, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044936279565680325, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044936279565680325, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 480.019, + "cuda_time_us": 134.36599999999999, + "pct_cuda_time": 1.9654648893626958, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 166.874, + "cuda_time_us": 81.759, + "pct_cuda_time": 1.1959457294955915, + "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": 81.759, + "pct_cuda_time": 1.1959457294955915, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 113.726, + "cuda_time_us": 9.023, + "pct_cuda_time": 0.13198569352901482, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13198569352901482, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.464, + "cuda_time_us": 43.584, + "pct_cuda_time": 0.6375334663380896, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6375334663380896, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2358.838, + "cuda_time_us": 196.89000000000001, + "pct_cuda_time": 2.8800469022417965, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.5, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04400010707472865, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04400010707472865, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1660.672, + "cuda_time_us": 57.181, + "pct_cuda_time": 0.8364262375798067, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 147.184, + "cuda_time_us": 20.447, + "pct_cuda_time": 0.29909248316388853, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.447, + "pct_cuda_time": 0.29909248316388853, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 464.194, + "cuda_time_us": 3.711, + "pct_cuda_time": 0.05428337678002593, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.711, + "pct_cuda_time": 0.05428337678002593, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 728.563, + "cuda_time_us": 15.232, + "pct_cuda_time": 0.22280905284649827, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.0369788133925911, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.424, + "pct_cuda_time": 0.1671067896348737, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 164.948, + "cuda_time_us": 17.791, + "pct_cuda_time": 0.26024132478939405, + "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": 0.228879546342513, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.03136177844688106, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 93.406, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04680862454758367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04680862454758367, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 458.642, + "cuda_time_us": 133.501, + "pct_cuda_time": 1.9528119330396772, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.006, + "cuda_time_us": 81.247, + "pct_cuda_time": 1.1884563495679783, + "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": 1.1884563495679783, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.892, + "cuda_time_us": 8.991, + "pct_cuda_time": 0.13151760728353898, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13151760728353898, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.794, + "cuda_time_us": 43.263, + "pct_cuda_time": 0.6328379761881601, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6328379761881601, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2448.52, + "cuda_time_us": 198.84699999999998, + "pct_cuda_time": 2.9086733016916777, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.584, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04446819332020449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04446819332020449, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1734.03, + "cuda_time_us": 57.983999999999995, + "pct_cuda_time": 0.8481722768022161, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 140.86, + "cuda_time_us": 21.504, + "pct_cuda_time": 0.3145539569597623, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.504, + "pct_cuda_time": 0.3145539569597623, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 506.911, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.05429800447519706, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.05429800447519706, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 764.393, + "cuda_time_us": 15.072, + "pct_cuda_time": 0.22046862161911907, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.036510727147115264, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.2, + "pct_cuda_time": 0.16383018591654283, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.020127708555460977, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 167.27, + "cuda_time_us": 17.695999999999998, + "pct_cuda_time": 0.25885169374813766, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.584, + "pct_cuda_time": 0.22795800154673246, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.796, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044936279565680325, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044936279565680325, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 480.792, + "cuda_time_us": 134.75099999999998, + "pct_cuda_time": 1.9710965520035768, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.699, + "cuda_time_us": 81.279, + "pct_cuda_time": 1.1889244358134539, + "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": 1.1889244358134539, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.045, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.13246840746966176, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.13246840746966176, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.145, + "cuda_time_us": 44.416, + "pct_cuda_time": 0.6497037087204613, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6497037087204613, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2710.591, + "cuda_time_us": 197.40499999999997, + "pct_cuda_time": 2.8875801652549224, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.964, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044936279565680325, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044936279565680325, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1767.844, + "cuda_time_us": 57.278, + "pct_cuda_time": 0.8378451240114054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 142.551, + "cuda_time_us": 20.639, + "pct_cuda_time": 0.3019010006367435, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.639, + "pct_cuda_time": 0.3019010006367435, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 469.66, + "cuda_time_us": 3.872, + "pct_cuda_time": 0.056638435702576234, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.056638435702576234, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 841.217, + "cuda_time_us": 14.975, + "pct_cuda_time": 0.21904973518752044, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.036510727147115264, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.199, + "pct_cuda_time": 0.16381555822137173, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 166.139, + "cuda_time_us": 17.792, + "pct_cuda_time": 0.26025595248456523, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.679, + "pct_cuda_time": 0.22934763258798888, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030908319896576338, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.838, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04446819332020449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04446819332020449, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 701.372, + "cuda_time_us": 134.015, + "pct_cuda_time": 1.9603305683576326, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.439, + "cuda_time_us": 81.631, + "pct_cuda_time": 1.1940733845136882, + "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": 1.1940733845136882, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.303, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.13059606248775846, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13059606248775846, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 374.204, + "cuda_time_us": 43.456, + "pct_cuda_time": 0.6356611213561862, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.456, + "pct_cuda_time": 0.6356611213561862, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2557.553, + "cuda_time_us": 199.06899999999996, + "pct_cuda_time": 2.911920650019666, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.705, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04634053830210783, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04634053830210783, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1818.976, + "cuda_time_us": 58.047, + "pct_cuda_time": 0.8490938215979966, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 149.223, + "cuda_time_us": 21.375, + "pct_cuda_time": 0.3126669842826878, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.375, + "pct_cuda_time": 0.3126669842826878, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 547.208, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05382991822972122, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05382991822972122, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 791.382, + "cuda_time_us": 15.2, + "pct_cuda_time": 0.2223409666010224, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03838307212901861, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.296, + "pct_cuda_time": 0.16523444465297035, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 163.603, + "cuda_time_us": 17.792, + "pct_cuda_time": 0.26025595248456523, + "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": 0.22936226028315998, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.854, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044936279565680325, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044936279565680325, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 498.681, + "cuda_time_us": 134.78199999999998, + "pct_cuda_time": 1.9715500105538817, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 181.014, + "cuda_time_us": 81.919, + "pct_cuda_time": 1.1982861607229707, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.919, + "pct_cuda_time": 1.1982861607229707, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.739, + "cuda_time_us": 9.312, + "pct_cuda_time": 0.13621309743346846, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.312, + "pct_cuda_time": 0.13621309743346846, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.029, + "cuda_time_us": 43.551, + "pct_cuda_time": 0.6370507523974427, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6370507523974427, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2346.548, + "cuda_time_us": 198.493, + "pct_cuda_time": 2.9034950976011014, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.365, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04400010707472865, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04400010707472865, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1666.848, + "cuda_time_us": 57.119, + "pct_cuda_time": 0.8355193204791974, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 138.539, + "cuda_time_us": 20.544, + "pct_cuda_time": 0.3005113695954872, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.3005113695954872, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 479.046, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05382991822972122, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05382991822972122, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 730.907, + "cuda_time_us": 15.072, + "pct_cuda_time": 0.22046862161911907, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.036510727147115264, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.264, + "pct_cuda_time": 0.1647663584074945, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.019191536064509306, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 155.98, + "cuda_time_us": 17.823, + "pct_cuda_time": 0.26070941103486994, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.679, + "pct_cuda_time": 0.22934763258798888, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.03136177844688106, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.107, + "cuda_time_us": 3.296, + "pct_cuda_time": 0.04821288328401118, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04821288328401118, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 456.647, + "cuda_time_us": 135.07, + "pct_cuda_time": 1.9757627867631644, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.615, + "cuda_time_us": 82.367, + "pct_cuda_time": 1.2048393681596326, + "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": 82.367, + "pct_cuda_time": 1.2048393681596326, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.237, + "cuda_time_us": 9.152, + "pct_cuda_time": 0.13387266620608929, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13387266620608929, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.761, + "cuda_time_us": 43.551, + "pct_cuda_time": 0.6370507523974427, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6370507523974427, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2487.516, + "cuda_time_us": 199.00400000000002, + "pct_cuda_time": 2.910969849833544, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 88.838, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04538973811598504, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04538973811598504, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1748.452, + "cuda_time_us": 57.95, + "pct_cuda_time": 0.8476749351663979, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 149.0, + "cuda_time_us": 21.472, + "pct_cuda_time": 0.31408587071428645, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.472, + "pct_cuda_time": 0.31408587071428645, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 463.074, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.05429800447519706, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.05429800447519706, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 777.219, + "cuda_time_us": 15.167, + "pct_cuda_time": 0.22185825266037548, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03931924461997029, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.199, + "pct_cuda_time": 0.16381555822137173, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 204.255, + "cuda_time_us": 17.599, + "pct_cuda_time": 0.25743280731653906, + "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": 15.487, + "pct_cuda_time": 0.22653911511513383, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.618, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.045418993506327276, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.045418993506327276, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 490.572, + "cuda_time_us": 134.846, + "pct_cuda_time": 1.9724861830448335, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 173.08, + "cuda_time_us": 82.174, + "pct_cuda_time": 1.2020162229916065, + "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": 82.174, + "pct_cuda_time": 1.2020162229916065, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 109.385, + "cuda_time_us": 9.44, + "pct_cuda_time": 0.13808544241537182, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.44, + "pct_cuda_time": 0.13808544241537182, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 147.29, + "cuda_time_us": 43.232, + "pct_cuda_time": 0.6323845176378554, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.232, + "pct_cuda_time": 0.6323845176378554, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2477.206, + "cuda_time_us": 197.243, + "pct_cuda_time": 2.8852104786372017, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.993, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04446819332020449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04446819332020449, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1783.644, + "cuda_time_us": 57.054, + "pct_cuda_time": 0.8345685202930745, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.416, + "cuda_time_us": 20.639, + "pct_cuda_time": 0.3019010006367435, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.639, + "pct_cuda_time": 0.3019010006367435, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 529.166, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05382991822972122, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05382991822972122, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 780.146, + "cuda_time_us": 15.007, + "pct_cuda_time": 0.2195178214329963, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.527, + "pct_cuda_time": 0.03696418569741998, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.2, + "pct_cuda_time": 0.16383018591654283, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 161.387, + "cuda_time_us": 17.728, + "pct_cuda_time": 0.25931977999361355, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.584, + "pct_cuda_time": 0.22795800154673246, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.03136177844688106, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.566, + "cuda_time_us": 3.071, + "pct_cuda_time": 0.044921651870509206, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.071, + "pct_cuda_time": 0.044921651870509206, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 469.763, + "cuda_time_us": 134.078, + "pct_cuda_time": 1.9612521131534135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 167.259, + "cuda_time_us": 81.631, + "pct_cuda_time": 1.1940733845136882, + "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": 1.1940733845136882, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.847, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.13246840746966176, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.13246840746966176, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.618, + "cuda_time_us": 43.391, + "pct_cuda_time": 0.6347103211700634, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.391, + "pct_cuda_time": 0.6347103211700634, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2373.551, + "cuda_time_us": 198.205, + "pct_cuda_time": 2.8992823213918193, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 91.978, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04540436581115616, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04540436581115616, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1670.473, + "cuda_time_us": 57.983000000000004, + "pct_cuda_time": 0.8481576491070449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 142.122, + "cuda_time_us": 21.536, + "pct_cuda_time": 0.31502204320523813, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.536, + "pct_cuda_time": 0.31502204320523813, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 480.162, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.05429800447519706, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.05429800447519706, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 749.237, + "cuda_time_us": 14.943, + "pct_cuda_time": 0.21858164894204463, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.036510727147115264, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.167, + "pct_cuda_time": 0.16334747197589589, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 152.91, + "cuda_time_us": 17.792, + "pct_cuda_time": 0.26025595248456523, + "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": 0.22936226028315998, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.358, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04446819332020449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04446819332020449, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 462.812, + "cuda_time_us": 134.078, + "pct_cuda_time": 1.9612521131534135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.888, + "cuda_time_us": 81.727, + "pct_cuda_time": 1.1954776432501157, + "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": 81.727, + "pct_cuda_time": 1.1954776432501157, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.222, + "cuda_time_us": 9.088, + "pct_cuda_time": 0.1329364937151376, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1329364937151376, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.38, + "cuda_time_us": 43.263, + "pct_cuda_time": 0.6328379761881601, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6328379761881601, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2427.885, + "cuda_time_us": 197.405, + "pct_cuda_time": 2.8875801652549233, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.098, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04400010707472865, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04400010707472865, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1732.768, + "cuda_time_us": 57.312, + "pct_cuda_time": 0.8383424656472235, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.56, + "cuda_time_us": 20.672, + "pct_cuda_time": 0.30238371457739055, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.30238371457739055, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 493.471, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05382991822972122, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05382991822972122, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 751.679, + "cuda_time_us": 15.04, + "pct_cuda_time": 0.22000053537364322, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.036510727147115264, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.232, + "pct_cuda_time": 0.16429827216201867, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.019191536064509306, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 168.164, + "cuda_time_us": 17.92, + "pct_cuda_time": 0.2621282974664686, + "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": 0.23076651901958747, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.03136177844688106, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.942, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04680862454758367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04680862454758367, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 473.274, + "cuda_time_us": 133.885, + "pct_cuda_time": 1.9584289679853872, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.131, + "cuda_time_us": 81.47, + "pct_cuda_time": 1.191718325591138, + "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": 81.47, + "pct_cuda_time": 1.191718325591138, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.453, + "cuda_time_us": 9.344, + "pct_cuda_time": 0.1366811836789443, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1366811836789443, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 149.722, + "cuda_time_us": 43.071, + "pct_cuda_time": 0.630029458715305, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.630029458715305, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2338.99, + "cuda_time_us": 198.718, + "pct_cuda_time": 2.9067863290146034, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.866, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.04540436581115616, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04540436581115616, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1659.562, + "cuda_time_us": 57.599, + "pct_cuda_time": 0.842540614161335, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.366, + "cuda_time_us": 21.376, + "pct_cuda_time": 0.31268161197785893, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.31268161197785893, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 468.08, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.0561703494571004, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.0561703494571004, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 740.719, + "cuda_time_us": 14.943, + "pct_cuda_time": 0.21858164894204463, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.036510727147115264, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.167, + "pct_cuda_time": 0.16334747197589589, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 155.729, + "cuda_time_us": 17.439999999999998, + "pct_cuda_time": 0.25510700378433093, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void cutlass::Kernel2(cutlass_80_tensorop_bf16_s16816gemm_relu_bf16_64x64_64x4_tn_align8::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.328, + "pct_cuda_time": 0.22421331158292576, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.326, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04680862454758367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04680862454758367, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 460.885, + "cuda_time_us": 134.815, + "pct_cuda_time": 1.9720327244945288, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 156.717, + "cuda_time_us": 81.343, + "pct_cuda_time": 1.1898606083044059, + "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": 1.1898606083044059, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.147, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.13340457996061345, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.13340457996061345, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.9, + "cuda_time_us": 44.352, + "pct_cuda_time": 0.6487675362295096, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.352, + "pct_cuda_time": 0.6487675362295096, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2740.908, + "cuda_time_us": 197.14999999999998, + "pct_cuda_time": 2.8838501029862873, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.146, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04400010707472865, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04400010707472865, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2028.507, + "cuda_time_us": 57.183, + "pct_cuda_time": 0.8364554929701491, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 161.011, + "cuda_time_us": 20.576, + "pct_cuda_time": 0.300979455840963, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.300979455840963, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 542.865, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.0561703494571004, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.0561703494571004, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 986.673, + "cuda_time_us": 15.007, + "pct_cuda_time": 0.2195178214329963, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.036510727147115264, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.199, + "pct_cuda_time": 0.16381555822137173, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.019191536064509306, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 173.004, + "cuda_time_us": 17.759999999999998, + "pct_cuda_time": 0.25978786623908934, + "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": 0.22889417403768414, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.505, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04446819332020449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04446819332020449, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 483.328, + "cuda_time_us": 133.91899999999998, + "pct_cuda_time": 1.958926309621205, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 175.495, + "cuda_time_us": 81.663, + "pct_cuda_time": 1.1945414707591642, + "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": 81.663, + "pct_cuda_time": 1.1945414707591642, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.901, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.13059606248775846, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13059606248775846, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 150.771, + "cuda_time_us": 43.328, + "pct_cuda_time": 0.6337887763742829, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.328, + "pct_cuda_time": 0.6337887763742829, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2408.68, + "cuda_time_us": 198.301, + "pct_cuda_time": 2.900686580128246, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.318, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04680862454758367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04680862454758367, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1689.089, + "cuda_time_us": 58.013999999999996, + "pct_cuda_time": 0.8486111076573497, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 138.496, + "cuda_time_us": 21.247, + "pct_cuda_time": 0.31079463930078444, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.247, + "pct_cuda_time": 0.31079463930078444, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 487.523, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.05429800447519706, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.05429800447519706, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 733.319, + "cuda_time_us": 15.2, + "pct_cuda_time": 0.2223409666010224, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.0369788133925911, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.264, + "pct_cuda_time": 0.1647663584074945, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.020595794800936814, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 173.027, + "cuda_time_us": 17.855, + "pct_cuda_time": 0.2611774972803457, + "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": 0.22983034652863582, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, void*, long, float*, int*)", + "cpu_time_us": 0, + "cuda_time_us": 2.143, + "pct_cuda_time": 0.031347150751709935, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.178, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04680862454758367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04680862454758367, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 496.626, + "cuda_time_us": 133.887, + "pct_cuda_time": 1.9584582233757295, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 186.251, + "cuda_time_us": 81.343, + "pct_cuda_time": 1.1898606083044059, + "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": 1.1898606083044059, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.562, + "cuda_time_us": 9.376, + "pct_cuda_time": 0.13714926992442014, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.376, + "pct_cuda_time": 0.13714926992442014, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.656, + "cuda_time_us": 43.168, + "pct_cuda_time": 0.6314483451469036, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.168, + "pct_cuda_time": 0.6314483451469036, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2548.278, + "cuda_time_us": 198.074, + "pct_cuda_time": 2.8973660933244028, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.517, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04446819332020449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04446819332020449, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1811.334, + "cuda_time_us": 56.893, + "pct_cuda_time": 0.8322134613705243, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.145, + "cuda_time_us": 20.416, + "pct_cuda_time": 0.2986390246135838, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.2986390246135838, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 492.633, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053361831984245385, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053361831984245385, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 766.295, + "cuda_time_us": 15.069999999999999, + "pct_cuda_time": 0.22043936622877683, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.527, + "pct_cuda_time": 0.03696418569741998, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.231, + "pct_cuda_time": 0.16428364446684757, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.019191536064509306, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 254.309, + "cuda_time_us": 17.759, + "pct_cuda_time": 0.25977323854391826, + "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": 0.228879546342513, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 90.557, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04634053830210783, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04634053830210783, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 499.714, + "cuda_time_us": 134.973, + "pct_cuda_time": 1.974343900331566, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 177.007, + "cuda_time_us": 81.502, + "pct_cuda_time": 1.1921864118366137, + "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": 81.502, + "pct_cuda_time": 1.1921864118366137, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.131, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.13340457996061345, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.13340457996061345, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.079, + "cuda_time_us": 44.351, + "pct_cuda_time": 0.6487529085343385, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.351, + "pct_cuda_time": 0.6487529085343385, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2472.267, + "cuda_time_us": 198.62400000000002, + "pct_cuda_time": 2.905411325668519, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 90.773, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04446819332020449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04446819332020449, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1759.666, + "cuda_time_us": 58.241, + "pct_cuda_time": 0.8519315944611938, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 141.37, + "cuda_time_us": 21.504, + "pct_cuda_time": 0.3145539569597623, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.504, + "pct_cuda_time": 0.3145539569597623, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 511.902, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.0561703494571004, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.0561703494571004, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 789.548, + "cuda_time_us": 15.104999999999999, + "pct_cuda_time": 0.220951335559766, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.593, + "pct_cuda_time": 0.03792961357871389, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.2, + "pct_cuda_time": 0.16383018591654283, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.019191536064509306, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 160.2, + "cuda_time_us": 17.792, + "pct_cuda_time": 0.26025595248456523, + "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": 0.2284260877922083, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.0318298646923569, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.222, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04634053830210783, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04634053830210783, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 468.535, + "cuda_time_us": 134.175, + "pct_cuda_time": 1.9626709995850125, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.921, + "cuda_time_us": 81.343, + "pct_cuda_time": 1.1898606083044059, + "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": 1.1898606083044059, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.473, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.13059606248775846, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13059606248775846, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.371, + "cuda_time_us": 43.904, + "pct_cuda_time": 0.642214328792848, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.904, + "pct_cuda_time": 0.642214328792848, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2483.514, + "cuda_time_us": 197.185, + "pct_cuda_time": 2.884362072317277, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.846, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04446819332020449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04446819332020449, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1740.563, + "cuda_time_us": 56.80200000000001, + "pct_cuda_time": 0.8308823411099524, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.938, + "cuda_time_us": 20.48, + "pct_cuda_time": 0.2995751971045355, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.2995751971045355, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[8, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 495.386, + "cuda_time_us": 3.713, + "pct_cuda_time": 0.054312632170368184, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.713, + "pct_cuda_time": 0.054312632170368184, + "trace": "_C::rotary_embedding(int64[8], bfloat16[8, 4096], bfloat16[8, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 761.019, + "cuda_time_us": 15.04, + "pct_cuda_time": 0.22000053537364322, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.036510727147115264, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[8], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.264, + "pct_cuda_time": 0.1647663584074945, + "trace": "_vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01872344981903347, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[8, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[8, 1, 32, 128], None, None, None, None, int32[8], None, None, int32[8, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[8, 32, 128], bfloat16[8, 8, 128], bfloat16[8, 8, 128], bfloat16[8, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 175.732, + "cuda_time_us": 17.569000000000003, + "pct_cuda_time": 0.2569939764614055, + "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": 15.457, + "pct_cuda_time": 0.22610028426000026, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 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": 0.030893692201405223, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[8, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.804, + "cuda_time_us": 3.296, + "pct_cuda_time": 0.04821288328401118, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04821288328401118, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 507.849, + "cuda_time_us": 134.047, + "pct_cuda_time": 1.9607986546031086, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 172.243, + "cuda_time_us": 80.607, + "pct_cuda_time": 1.1790946246584615, + "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": 1.1790946246584615, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[8, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 105.36, + "cuda_time_us": 9.088, + "pct_cuda_time": 0.1329364937151376, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1329364937151376, + "trace": "_C::silu_and_mul(bfloat16[8, 14336], bfloat16[8, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 170.064, + "cuda_time_us": 44.352, + "pct_cuda_time": 0.6487675362295096, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.352, + "pct_cuda_time": 0.6487675362295096, + "trace": "mm(bfloat16[8, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[8, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[8, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.907, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04400010707472865, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04400010707472865, + "trace": "_C::fused_add_rms_norm(bfloat16[8, 4096], bfloat16[8, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 534.195, + "cuda_time_us": 349.083, + "pct_cuda_time": 5.106279713420047, + "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": 5.6, + "pct_cuda_time": 0.08191509295827142, + "trace": "index_select(bfloat16[8, 4096], 0, int64[8])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.010765983645944244, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[8, 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": 342.747, + "pct_cuda_time": 5.013598636815832, + "trace": "mm(bfloat16[8, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[8, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[8, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 3857.672, + "cuda_time_us": 119.422, + "pct_cuda_time": 1.7468686127254802, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.010765983645944244, + "trace": "copy_(bfloat16[8], bfloat16[8], True) <- _to_copy(bfloat16[8], 15, 0, None, None, True, None) <- to(bfloat16[8], 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": 0.010765983645944244, + "trace": "copy_(bfloat16[8], bfloat16[8], True) <- _to_copy(bfloat16[8], 15, 0, None, None, True, None) <- to(bfloat16[8], 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": 0.011219442196248961, + "trace": "copy_(int32[8], int32[8], True) <- _to_copy(int32[8], 3, 0, None, None, True, None) <- to(int32[8], 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": 0.011234069891420081, + "trace": "copy_(bfloat16[8], bfloat16[8], True) <- _to_copy(bfloat16[8], 15, 0, None, None, True, None) <- to(bfloat16[8], 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": 0.011234069891420081, + "trace": "copy_(bfloat16[8], bfloat16[8], True) <- _to_copy(bfloat16[8], 15, 0, None, None, True, None) <- to(bfloat16[8], 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": 0.011702156136895918, + "trace": "copy_(bfloat16[8], bfloat16[8], True) <- _to_copy(bfloat16[8], 15, 0, None, None, True, None) <- to(bfloat16[8], 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": 0.011702156136895918, + "trace": "copy_(bfloat16[8], bfloat16[8], True) <- _to_copy(bfloat16[8], 15, 0, None, None, True, None) <- to(bfloat16[8], 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": 4.833, + "pct_cuda_time": 0.07069565076202246, + "trace": "copy_(float32[8, 128256], bfloat16[8, 128256], False) <- _to_copy(bfloat16[8, 128256], 6, None, None, None, False, None) <- to(bfloat16[8, 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": 6.4, + "pct_cuda_time": 0.09361724909516735, + "trace": "div_(float32[8, 128256], bfloat16[8, 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": 34.976, + "pct_cuda_time": 0.5116182663050894, + "trace": "_softmax(float32[8, 128256], -1, False) <- softmax(float32[8, 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": 28.351, + "pct_cuda_time": 0.4147097857964202, + "trace": "_log_softmax(float32[8, 128256], -1, False) <- log_softmax(float32[8, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 2.016, + "pct_cuda_time": 0.02948943346497771, + "trace": "copy_(int64[8], int32[8], False) <- _to_copy(int32[8], 4, None, None, None, False, None) <- to(int32[8], 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": 7.392, + "pct_cuda_time": 0.10812792270491828, + "trace": "index(float32[8, 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": 27.615, + "pct_cuda_time": 0.40394380215047593, + "trace": "argmax(float32[8, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 2.464, + "pct_cuda_time": 0.036042640901639424, + "trace": "copy_(int64[8], int64[8], False) <- _to_copy(int64[8], 4, 0, None, None, False, None) <- to(int64[8], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + } +} \ No newline at end of file