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"pct_cuda_time": 0.02348663796721299, "invocations": 1 }, "children": [] }, { "entry": { "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::SoftMaxForwardEpilogue>(float*, float const*, int)", "cuda_time_us": 35.391, "pct_cuda_time": 0.06289464318232711, "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": 29.407, "pct_cuda_time": 0.052260257468358995, "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": 2.08, "pct_cuda_time": 0.0036964442321279533, "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": 12.831, "pct_cuda_time": 0.022802440356939308, "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": 28.0, "pct_cuda_time": 0.049759826201722444, "invocations": 1 }, "children": [] }, { "entry": { "name": "Memcpy DtoH (Device -> Pageable)", "cuda_time_us": 2.784, "pct_cuda_time": 0.00494754843377126, "invocations": 1 }, "children": [] } ] } ], "model_stats": [ { "entry": { "name": "LlamaForCausalLM", "cpu_time_us": 87562.993, "cuda_time_us": 55735.13500000002, "pct_cuda_time": 99.04895110462637, "trace": "" }, "children": [ { "entry": { "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", "cpu_time_us": 330.891, "cuda_time_us": 72.671, "pct_cuda_time": 0.12914629749662043, "trace": "" }, "children": [ { "entry": { "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", "cpu_time_us": 0, "cuda_time_us": 72.671, "pct_cuda_time": 0.12914629749662043, "trace": "index_select(bfloat16[128256, 4096], 0, int64[2560]) <- embedding(bfloat16[128256, 4096], int64[2560], -1, False, False)" }, "children": [] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 4104.859, "cuda_time_us": 1754.216, "pct_cuda_time": 3.117481545724312, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 236.025, "cuda_time_us": 37.792, "pct_cuda_time": 0.06716154827912481, "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": 37.792, "pct_cuda_time": 0.06716154827912481, "trace": "_C::rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 3010.865, "cuda_time_us": 388.02600000000007, "pct_cuda_time": 0.6895752257767699, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 413.062, "cuda_time_us": 172.73200000000003, "pct_cuda_time": 0.3069683678384258, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.735, "pct_cuda_time": 0.0013061954377952142, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 171.997, "pct_cuda_time": 0.3056621724006306, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 990.272, "cuda_time_us": 33.824, "pct_cuda_time": 0.06010987005168071, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.824, "pct_cuda_time": 0.06010987005168071, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 1059.999, "cuda_time_us": 51.327000000000005, "pct_cuda_time": 0.09121509283770743, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.96, "pct_cuda_time": 0.023031690984797244, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 37.087, "pct_cuda_time": 0.06590866694083145, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0022747349120787403, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 297.3, "cuda_time_us": 130.143, "pct_cuda_time": 0.23128189504895585, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 129.407, "pct_cuda_time": 0.2299739224745106, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 116.996, "cuda_time_us": 25.952, "pct_cuda_time": 0.04612025034239647, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 25.952, "pct_cuda_time": 0.04612025034239647, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 620.099, "cuda_time_us": 1302.446, "pct_cuda_time": 2.3146245213260213, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 190.164, "cuda_time_us": 780.533, "pct_cuda_time": 1.387113800882465, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 779.797, "pct_cuda_time": 1.38580582830802, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 187.787, "cuda_time_us": 109.727, "pct_cuda_time": 0.19499987320129997, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.727, "pct_cuda_time": 0.19499987320129997, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 174.709, "cuda_time_us": 412.186, "pct_cuda_time": 0.732510847242256, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 411.45, "pct_cuda_time": 0.7312028746678106, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2612.939, "cuda_time_us": 1738.3129999999999, "pct_cuda_time": 3.0892197415783835, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 78.841, "cuda_time_us": 26.528, "pct_cuda_time": 0.04714388105283189, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.528, "pct_cuda_time": 0.04714388105283189, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1848.632, "cuda_time_us": 383.068, "pct_cuda_time": 0.6807641822657647, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 155.239, "cuda_time_us": 167.07, "pct_cuda_time": 0.29690622012577744, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.334, "pct_cuda_time": 0.2955982475513322, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 525.803, "cuda_time_us": 34.112, "pct_cuda_time": 0.060621685406898426, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 34.112, "pct_cuda_time": 0.060621685406898426, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 790.825, "cuda_time_us": 50.911, "pct_cuda_time": 0.09047580399128184, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 13.152, "pct_cuda_time": 0.023372901221609056, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.287, "pct_cuda_time": 0.06448695762078221, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.002615945148890551, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 222.608, "cuda_time_us": 130.975, "pct_cuda_time": 0.23276047274180703, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 130.239, "pct_cuda_time": 0.2314525001673618, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 89.811, "cuda_time_us": 26.079, "pct_cuda_time": 0.04634594669695427, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.079, "pct_cuda_time": 0.04634594669695427, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 510.67, "cuda_time_us": 1302.638, "pct_cuda_time": 2.3149657315628325, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 167.046, "cuda_time_us": 779.925, "pct_cuda_time": 1.3860333017992277, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 779.189, "pct_cuda_time": 1.3847253292247823, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 111.957, "cuda_time_us": 109.887, "pct_cuda_time": 0.1952842150653098, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.887, "pct_cuda_time": 0.1952842150653098, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 178.509, "cuda_time_us": 412.82599999999996, "pct_cuda_time": 0.7336482146982953, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 412.058, "pct_cuda_time": 0.732283373751048, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2588.022, "cuda_time_us": 1745.993, "pct_cuda_time": 3.1028681510508562, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 73.25, "cuda_time_us": 26.08, "pct_cuda_time": 0.04634772383360433, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.08, "pct_cuda_time": 0.04634772383360433, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1874.174, "cuda_time_us": 384.38, "pct_cuda_time": 0.6830957855506454, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 147.027, "cuda_time_us": 168.31799999999998, "pct_cuda_time": 0.2991240866650542, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 167.582, "pct_cuda_time": 0.29781611409060893, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 544.543, "cuda_time_us": 33.823, "pct_cuda_time": 0.06010809291503065, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.823, "pct_cuda_time": 0.06010809291503065, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 801.661, "cuda_time_us": 51.678999999999995, "pct_cuda_time": 0.09184064493852907, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.895, "pct_cuda_time": 0.022916177102543248, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 37.312, "pct_cuda_time": 0.06630852268709528, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.002615945148890551, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 207.489, "cuda_time_us": 130.56, "pct_cuda_time": 0.23202296103203152, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.769, "pct_cuda_time": 0.0013666180838973058, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 129.791, "pct_cuda_time": 0.23065634294813422, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 81.686, "cuda_time_us": 27.359, "pct_cuda_time": 0.04862068160903302, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 27.359, "pct_cuda_time": 0.04862068160903302, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 468.68, "cuda_time_us": 1308.174, "pct_cuda_time": 2.324803960057573, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 166.93, "cuda_time_us": 785.365, "pct_cuda_time": 1.3957009251755625, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 784.629, "pct_cuda_time": 1.3943929526011172, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 97.55, "cuda_time_us": 109.311, "pct_cuda_time": 0.19426058435487437, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.311, "pct_cuda_time": 0.19426058435487437, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 151.305, "cuda_time_us": 413.498, "pct_cuda_time": 0.7348424505271367, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 412.73, "pct_cuda_time": 0.7334776095798894, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 5463.612, "cuda_time_us": 1747.239, "pct_cuda_time": 3.105082463316833, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 72.726, "cuda_time_us": 26.496, "pct_cuda_time": 0.047087012680029926, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.496, "pct_cuda_time": 0.047087012680029926, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1826.112, "cuda_time_us": 382.874, "pct_cuda_time": 0.6804194177556528, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 151.904, "cuda_time_us": 167.869, "pct_cuda_time": 0.29832615230917664, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 167.101, "pct_cuda_time": 0.2969613113619293, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 494.074, "cuda_time_us": 34.144, "pct_cuda_time": 0.0606785537797004, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 34.144, "pct_cuda_time": 0.0606785537797004, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 749.465, "cuda_time_us": 50.782999999999994, "pct_cuda_time": 0.09024833050007394, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.928, "pct_cuda_time": 0.02297482261199528, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.607, "pct_cuda_time": 0.0650556413488019, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", "cpu_time_us": 0, "cuda_time_us": 1.248, "pct_cuda_time": 0.0022178665392767714, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 228.721, "cuda_time_us": 130.078, "pct_cuda_time": 0.23116638116670185, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 129.342, "pct_cuda_time": 0.22985840859225662, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 86.877, "cuda_time_us": 26.847, "pct_cuda_time": 0.04771078764420152, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.847, "pct_cuda_time": 0.04771078764420152, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 3397.544, "cuda_time_us": 1311.022, "pct_cuda_time": 2.3298652452369484, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 168.439, "cuda_time_us": 783.509, "pct_cuda_time": 1.3924025595530483, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 782.773, "pct_cuda_time": 1.391094586978603, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 97.136, "cuda_time_us": 109.598, "pct_cuda_time": 0.19477062257344202, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.598, "pct_cuda_time": 0.19477062257344202, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 3070.706, "cuda_time_us": 417.915, "pct_cuda_time": 0.7426920631104584, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.737, "pct_cuda_time": 0.0013097497110953372, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 417.178, "pct_cuda_time": 0.741382313399363, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 3003.454, "cuda_time_us": 1742.6329999999998, "pct_cuda_time": 3.0968969719066495, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 155.339, "cuda_time_us": 25.632, "pct_cuda_time": 0.04555156661437678, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 25.632, "pct_cuda_time": 0.04555156661437678, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 2157.299, "cuda_time_us": 385.948, "pct_cuda_time": 0.6858823358179419, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 183.146, "cuda_time_us": 172.285, "pct_cuda_time": 0.30617398775584825, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 171.549, "pct_cuda_time": 0.304866015181403, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 661.823, "cuda_time_us": 32.896, "pct_cuda_time": 0.05846068724042363, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 32.896, "pct_cuda_time": 0.05846068724042363, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 911.811, "cuda_time_us": 51.072, "pct_cuda_time": 0.09076192299194175, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.896, "pct_cuda_time": 0.022917954239193312, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.896, "pct_cuda_time": 0.06556923384066969, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0022747349120787403, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 212.033, "cuda_time_us": 129.695, "pct_cuda_time": 0.23048573782972828, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.959, "pct_cuda_time": 0.22917776525528305, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 83.867, "cuda_time_us": 26.175, "pct_cuda_time": 0.04651655181536018, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.175, "pct_cuda_time": 0.04651655181536018, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 498.106, "cuda_time_us": 1304.878, "pct_cuda_time": 2.3189465176589703, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 175.555, "cuda_time_us": 782.773, "pct_cuda_time": 1.391094586978603, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 782.037, "pct_cuda_time": 1.3897866144041577, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 113.113, "cuda_time_us": 109.631, "pct_cuda_time": 0.19482926808289405, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.631, "pct_cuda_time": 0.19482926808289405, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 148.854, "cuda_time_us": 412.474, "pct_cuda_time": 0.7330226625974737, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 411.738, "pct_cuda_time": 0.7317146900230285, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2886.168, "cuda_time_us": 1741.187, "pct_cuda_time": 3.0943272323106603, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 70.452, "cuda_time_us": 26.304, "pct_cuda_time": 0.04674580244321811, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.304, "pct_cuda_time": 0.04674580244321811, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 2106.337, "cuda_time_us": 382.29599999999994, "pct_cuda_time": 0.6793922327719171, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 156.12, "cuda_time_us": 167.326, "pct_cuda_time": 0.2973611671081932, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.59, "pct_cuda_time": 0.29605319453374795, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 739.163, "cuda_time_us": 34.175, "pct_cuda_time": 0.060733645015852294, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 34.175, "pct_cuda_time": 0.060733645015852294, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 801.261, "cuda_time_us": 51.357, "pct_cuda_time": 0.09126840693720926, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.959, "pct_cuda_time": 0.023029913848147184, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.927, "pct_cuda_time": 0.06562432507682159, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", "cpu_time_us": 0, "cuda_time_us": 1.471, "pct_cuda_time": 0.00261416801224049, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 239.015, "cuda_time_us": 129.438, "pct_cuda_time": 0.23002901371066248, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.702, "pct_cuda_time": 0.22872104113621722, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 92.443, "cuda_time_us": 26.655, "pct_cuda_time": 0.0473695774073897, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.655, "pct_cuda_time": 0.0473695774073897, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 518.912, "cuda_time_us": 1305.932, "pct_cuda_time": 2.3208196196881357, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 195.858, "cuda_time_us": 783.317, "pct_cuda_time": 1.3920613493162364, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 782.581, "pct_cuda_time": 1.3907533767417912, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 105.171, "cuda_time_us": 109.63, "pct_cuda_time": 0.19482749094624396, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.63, "pct_cuda_time": 0.19482749094624396, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 163.055, "cuda_time_us": 412.985, "pct_cuda_time": 0.7339307794256552, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.735, "pct_cuda_time": 0.0013061954377952142, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 412.25, "pct_cuda_time": 0.7326245839878599, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2527.754, "cuda_time_us": 1740.327, "pct_cuda_time": 3.0927988947916076, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 110.457, "cuda_time_us": 25.567, "pct_cuda_time": 0.04543605273212278, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 25.567, "pct_cuda_time": 0.04543605273212278, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1787.463, "cuda_time_us": 382.682, "pct_cuda_time": 0.6800782075188411, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 150.355, "cuda_time_us": 167.07, "pct_cuda_time": 0.29690622012577744, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.334, "pct_cuda_time": 0.2955982475513322, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 504.495, "cuda_time_us": 34.4, "pct_cuda_time": 0.06113350076211615, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 34.4, "pct_cuda_time": 0.06113350076211615, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 783.367, "cuda_time_us": 51.582, "pct_cuda_time": 0.09166826268347311, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 13.119, "pct_cuda_time": 0.023314255712157027, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.959, "pct_cuda_time": 0.06568119344962357, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0026728135216925195, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 199.974, "cuda_time_us": 129.63, "pct_cuda_time": 0.23037022394747428, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.894, "pct_cuda_time": 0.22906225137302905, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 82.213, "cuda_time_us": 27.136, "pct_cuda_time": 0.04822438013606929, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 27.136, "pct_cuda_time": 0.04822438013606929, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 466.503, "cuda_time_us": 1304.942, "pct_cuda_time": 2.3190602544045746, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 164.463, "cuda_time_us": 781.525, "pct_cuda_time": 1.388876720439326, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 780.789, "pct_cuda_time": 1.3875687478648808, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 96.077, "cuda_time_us": 109.886, "pct_cuda_time": 0.19528243792865974, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.886, "pct_cuda_time": 0.19528243792865974, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 154.523, "cuda_time_us": 413.531, "pct_cuda_time": 0.7349010960365887, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 412.795, "pct_cuda_time": 0.7335931234621434, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2440.3, "cuda_time_us": 1735.048, "pct_cuda_time": 3.083417390415933, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 71.073, "cuda_time_us": 26.591, "pct_cuda_time": 0.04725584066178577, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.591, "pct_cuda_time": 0.04725584066178577, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1725.693, "cuda_time_us": 380.188, "pct_cuda_time": 0.6756460287135876, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 147.988, "cuda_time_us": 166.59, "pct_cuda_time": 0.29605319453374795, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.737, "pct_cuda_time": 0.0013097497110953372, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 165.853, "pct_cuda_time": 0.2947434448226526, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 487.704, "cuda_time_us": 33.888, "pct_cuda_time": 0.06022360679728465, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.888, "pct_cuda_time": 0.06022360679728465, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 729.164, "cuda_time_us": 50.495, "pct_cuda_time": 0.08973651514485624, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.607, "pct_cuda_time": 0.02240436174732553, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.608, "pct_cuda_time": 0.06505741848545198, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0022747349120787403, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 202.714, "cuda_time_us": 129.215, "pct_cuda_time": 0.22963271223769877, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.479, "pct_cuda_time": 0.22832473966325353, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 86.042, "cuda_time_us": 26.079, "pct_cuda_time": 0.04634594669695427, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.079, "pct_cuda_time": 0.04634594669695427, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 485.099, "cuda_time_us": 1302.19, "pct_cuda_time": 2.3141695743436053, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 174.66, "cuda_time_us": 780.949, "pct_cuda_time": 1.3878530897288905, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 780.213, "pct_cuda_time": 1.3865451171544454, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 104.652, "cuda_time_us": 109.759, "pct_cuda_time": 0.1950567415741019, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.759, "pct_cuda_time": 0.1950567415741019, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 153.009, "cuda_time_us": 411.48199999999997, "pct_cuda_time": 0.7312597430406126, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 410.746, "pct_cuda_time": 0.7299517704661673, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2525.07, "cuda_time_us": 1735.785, "pct_cuda_time": 3.084727140127028, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 72.997, "cuda_time_us": 25.919, "pct_cuda_time": 0.04606160483294443, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 25.919, "pct_cuda_time": 0.04606160483294443, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1775.686, "cuda_time_us": 380.443, "pct_cuda_time": 0.6760991985593532, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 165.588, "cuda_time_us": 167.421, "pct_cuda_time": 0.29752999508994904, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.685, "pct_cuda_time": 0.2962220225155038, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 498.171, "cuda_time_us": 33.696, "pct_cuda_time": 0.05988239656047283, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.696, "pct_cuda_time": 0.05988239656047283, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 742.376, "cuda_time_us": 50.624, "pct_cuda_time": 0.08996576577271419, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.96, "pct_cuda_time": 0.023031690984797244, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.256, "pct_cuda_time": 0.06443186638463032, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.002502208403286614, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 222.113, "cuda_time_us": 128.702, "pct_cuda_time": 0.22872104113621722, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 127.966, "pct_cuda_time": 0.22741306856177193, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 75.919, "cuda_time_us": 27.136, "pct_cuda_time": 0.04822438013606929, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 27.136, "pct_cuda_time": 0.04822438013606929, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 530.729, "cuda_time_us": 1302.287, "pct_cuda_time": 2.3143419565986614, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 159.501, "cuda_time_us": 781.462, "pct_cuda_time": 1.3887647608303724, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 780.726, "pct_cuda_time": 1.387456788255927, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 99.972, "cuda_time_us": 109.95, "pct_cuda_time": 0.19539617467426365, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.95, "pct_cuda_time": 0.19539617467426365, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 219.823, "cuda_time_us": 410.875, "pct_cuda_time": 0.7301810210940253, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 410.139, "pct_cuda_time": 0.72887304851958, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2431.968, "cuda_time_us": 1739.848, "pct_cuda_time": 3.091947646336228, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 76.842, "cuda_time_us": 26.816, "pct_cuda_time": 0.04765569640804961, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.816, "pct_cuda_time": 0.04765569640804961, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1732.02, "cuda_time_us": 380.09000000000003, "pct_cuda_time": 0.6754718693218816, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 150.3, "cuda_time_us": 166.46099999999998, "pct_cuda_time": 0.29582394390588995, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 165.725, "pct_cuda_time": 0.2945159713314447, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 477.312, "cuda_time_us": 33.632, "pct_cuda_time": 0.059768659814868896, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.632, "pct_cuda_time": 0.059768659814868896, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 773.27, "cuda_time_us": 51.199000000000005, "pct_cuda_time": 0.09098761934649956, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.896, "pct_cuda_time": 0.022917954239193312, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.831, "pct_cuda_time": 0.0654537199584157, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.002615945148890551, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 192.641, "cuda_time_us": 128.798, "pct_cuda_time": 0.2288916462546231, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.062, "pct_cuda_time": 0.22758367368017787, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 76.757, "cuda_time_us": 26.304, "pct_cuda_time": 0.04674580244321811, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.304, "pct_cuda_time": 0.04674580244321811, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 479.101, "cuda_time_us": 1306.638, "pct_cuda_time": 2.3220742781630785, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 161.172, "cuda_time_us": 782.837, "pct_cuda_time": 1.3912083237242068, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 782.101, "pct_cuda_time": 1.3899003511497616, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 121.128, "cuda_time_us": 109.598, "pct_cuda_time": 0.19477062257344202, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.598, "pct_cuda_time": 0.19477062257344202, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 148.416, "cuda_time_us": 414.203, "pct_cuda_time": 0.73609533186543, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 413.467, "pct_cuda_time": 0.7347873592909847, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2410.953, "cuda_time_us": 1736.453, "pct_cuda_time": 3.085914267409269, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 70.951, "cuda_time_us": 27.583, "pct_cuda_time": 0.04901876021864679, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 27.583, "pct_cuda_time": 0.04901876021864679, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1731.876, "cuda_time_us": 380.986, "pct_cuda_time": 0.6770641837603367, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 140.846, "cuda_time_us": 167.422, "pct_cuda_time": 0.2975317722265991, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.654, "pct_cuda_time": 0.29616693127935184, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 511.106, "cuda_time_us": 34.112, "pct_cuda_time": 0.060621685406898426, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 34.112, "pct_cuda_time": 0.060621685406898426, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 743.478, "cuda_time_us": 51.261, "pct_cuda_time": 0.09109780181880336, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.863, "pct_cuda_time": 0.02285930872974128, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.959, "pct_cuda_time": 0.06568119344962357, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", "cpu_time_us": 0, "cuda_time_us": 1.439, "pct_cuda_time": 0.0025572996394385215, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 194.644, "cuda_time_us": 128.191, "pct_cuda_time": 0.22781292430803576, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 127.423, "pct_cuda_time": 0.22644808336078856, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 81.578, "cuda_time_us": 26.496, "pct_cuda_time": 0.047087012680029926, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.496, "pct_cuda_time": 0.047087012680029926, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 462.031, "cuda_time_us": 1301.388, "pct_cuda_time": 2.312744310750256, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 163.376, "cuda_time_us": 779.636, "pct_cuda_time": 1.38551970930736, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.735, "pct_cuda_time": 0.0013061954377952142, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 778.901, "pct_cuda_time": 1.3842135138695648, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 96.631, "cuda_time_us": 109.918, "pct_cuda_time": 0.19533930630146173, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.918, "pct_cuda_time": 0.19533930630146173, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 153.56, "cuda_time_us": 411.834, "pct_cuda_time": 0.7318852951414343, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 411.098, "pct_cuda_time": 0.730577322566989, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2535.303, "cuda_time_us": 1735.914, "pct_cuda_time": 3.0849563907548863, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 74.242, "cuda_time_us": 26.399, "pct_cuda_time": 0.04691463042497396, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.399, "pct_cuda_time": 0.04691463042497396, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1834.955, "cuda_time_us": 380.98799999999994, "pct_cuda_time": 0.6770677380336366, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 148.95, "cuda_time_us": 167.03799999999998, "pct_cuda_time": 0.2968493517529755, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.302, "pct_cuda_time": 0.2955413791785302, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 510.446, "cuda_time_us": 34.175, "pct_cuda_time": 0.060733645015852294, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 34.175, "pct_cuda_time": 0.060733645015852294, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 748.043, "cuda_time_us": 50.497, "pct_cuda_time": 0.08974006941815638, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.801, "pct_cuda_time": 0.022749126257437465, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.416, "pct_cuda_time": 0.06471620824864016, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0022747349120787403, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 199.667, "cuda_time_us": 129.278, "pct_cuda_time": 0.22974467184665262, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.542, "pct_cuda_time": 0.22843669927220736, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 80.89, "cuda_time_us": 26.144, "pct_cuda_time": 0.04646146057920827, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.144, "pct_cuda_time": 0.04646146057920827, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 474.207, "cuda_time_us": 1302.383, "pct_cuda_time": 2.314512561717067, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 159.963, "cuda_time_us": 780.437, "pct_cuda_time": 1.3869431957640592, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 779.701, "pct_cuda_time": 1.385635223189614, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 113.591, "cuda_time_us": 109.599, "pct_cuda_time": 0.19477239971009208, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.599, "pct_cuda_time": 0.19477239971009208, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 152.083, "cuda_time_us": 412.347, "pct_cuda_time": 0.7327969662429159, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 411.579, "pct_cuda_time": 0.7314321252956686, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2677.625, "cuda_time_us": 1739.049, "pct_cuda_time": 3.090527714152829, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 70.059, "cuda_time_us": 25.984, "pct_cuda_time": 0.04617711871519843, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 25.984, "pct_cuda_time": 0.04617711871519843, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 2004.791, "cuda_time_us": 380.571, "pct_cuda_time": 0.6763266720505612, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 150.04, "cuda_time_us": 166.365, "pct_cuda_time": 0.2956533387874841, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.735, "pct_cuda_time": 0.0013061954377952142, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 165.63, "pct_cuda_time": 0.29434714334968887, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 500.398, "cuda_time_us": 34.432, "pct_cuda_time": 0.06119036913491811, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 34.432, "pct_cuda_time": 0.06119036913491811, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 1003.732, "cuda_time_us": 50.816, "pct_cuda_time": 0.09030697600952599, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.544, "pct_cuda_time": 0.022292402138371657, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.992, "pct_cuda_time": 0.06573983895907559, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0022747349120787403, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 208.884, "cuda_time_us": 128.958, "pct_cuda_time": 0.22917598811863296, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.222, "pct_cuda_time": 0.22786801554418773, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 79.761, "cuda_time_us": 26.527, "pct_cuda_time": 0.047142103916181836, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.527, "pct_cuda_time": 0.047142103916181836, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 446.364, "cuda_time_us": 1305.967, "pct_cuda_time": 2.320881819470888, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 159.15, "cuda_time_us": 783.157, "pct_cuda_time": 1.3917770074522267, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 782.421, "pct_cuda_time": 1.3904690348777813, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 94.667, "cuda_time_us": 109.791, "pct_cuda_time": 0.19511360994690388, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.791, "pct_cuda_time": 0.19511360994690388, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 142.398, "cuda_time_us": 413.019, "pct_cuda_time": 0.7339912020717573, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.737, "pct_cuda_time": 0.0013097497110953372, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 412.282, "pct_cuda_time": 0.7326814523606618, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2504.754, "cuda_time_us": 1733.799, "pct_cuda_time": 3.0811977467400062, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 64.924, "cuda_time_us": 26.591, "pct_cuda_time": 0.04725584066178577, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.591, "pct_cuda_time": 0.04725584066178577, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1788.34, "cuda_time_us": 380.12100000000004, "pct_cuda_time": 0.6755269605580335, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 148.492, "cuda_time_us": 166.55700000000002, "pct_cuda_time": 0.29599454902429595, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.735, "pct_cuda_time": 0.0013061954377952142, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 165.822, "pct_cuda_time": 0.2946883535865007, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 495.327, "cuda_time_us": 33.888, "pct_cuda_time": 0.06022360679728465, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.888, "pct_cuda_time": 0.06022360679728465, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 786.863, "cuda_time_us": 50.846999999999994, "pct_cuda_time": 0.09036206724567789, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.736, "pct_cuda_time": 0.02263361237518347, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.608, "pct_cuda_time": 0.06505741848545198, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0026710363850424583, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 213.44, "cuda_time_us": 128.829, "pct_cuda_time": 0.22894673749077504, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.735, "pct_cuda_time": 0.0013061954377952142, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.094, "pct_cuda_time": 0.22764054205297982, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 99.086, "cuda_time_us": 26.368, "pct_cuda_time": 0.04685953918882205, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.368, "pct_cuda_time": 0.04685953918882205, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 486.4, "cuda_time_us": 1300.719, "pct_cuda_time": 2.311555406331365, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 171.577, "cuda_time_us": 779.413, "pct_cuda_time": 1.3851234078343961, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 778.677, "pct_cuda_time": 1.383815435259951, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 100.161, "cuda_time_us": 109.471, "pct_cuda_time": 0.19454492621888422, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.471, "pct_cuda_time": 0.19454492621888422, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 162.711, "cuda_time_us": 411.835, "pct_cuda_time": 0.7318870722780844, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 411.067, "pct_cuda_time": 0.7305222313308372, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2435.228, "cuda_time_us": 1737.925, "pct_cuda_time": 3.0885302125581595, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 72.947, "cuda_time_us": 26.271, "pct_cuda_time": 0.046687156933766086, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.271, "pct_cuda_time": 0.046687156933766086, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1753.059, "cuda_time_us": 381.01700000000005, "pct_cuda_time": 0.6771192749964887, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 147.172, "cuda_time_us": 167.9, "pct_cuda_time": 0.2983812435453285, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.735, "pct_cuda_time": 0.0013061954377952142, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 167.165, "pct_cuda_time": 0.29707504810753327, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 522.962, "cuda_time_us": 33.632, "pct_cuda_time": 0.059768659814868896, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.632, "pct_cuda_time": 0.059768659814868896, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 739.119, "cuda_time_us": 50.943000000000005, "pct_cuda_time": 0.09053267236408381, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.575, "pct_cuda_time": 0.02234749337452356, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.896, "pct_cuda_time": 0.06556923384066969, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.002615945148890551, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 191.473, "cuda_time_us": 128.542, "pct_cuda_time": 0.22843669927220736, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 127.806, "pct_cuda_time": 0.22712872669776207, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 87.607, "cuda_time_us": 26.624, "pct_cuda_time": 0.04731448617123779, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.624, "pct_cuda_time": 0.04731448617123779, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 453.784, "cuda_time_us": 1304.013, "pct_cuda_time": 2.317409294456667, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 158.659, "cuda_time_us": 782.549, "pct_cuda_time": 1.3906965083689893, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 781.813, "pct_cuda_time": 1.389388535794544, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 95.05, "cuda_time_us": 109.823, "pct_cuda_time": 0.19517047831970585, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.823, "pct_cuda_time": 0.19517047831970585, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 151.972, "cuda_time_us": 411.641, "pct_cuda_time": 0.7315423077679726, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.767, "pct_cuda_time": 0.0013630638105971826, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 410.874, "pct_cuda_time": 0.7301792439573753, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2413.609, "cuda_time_us": 1736.36, "pct_cuda_time": 3.085748993700814, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 73.126, "cuda_time_us": 26.399, "pct_cuda_time": 0.04691463042497396, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.399, "pct_cuda_time": 0.04691463042497396, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1711.685, "cuda_time_us": 381.786, "pct_cuda_time": 0.6784858930803859, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 156.918, "cuda_time_us": 166.846, "pct_cuda_time": 0.29650814151616367, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.078, "pct_cuda_time": 0.2951433005689164, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 506.254, "cuda_time_us": 34.207, "pct_cuda_time": 0.06079051338865427, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 34.207, "pct_cuda_time": 0.06079051338865427, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 717.196, "cuda_time_us": 51.263, "pct_cuda_time": 0.09110135609210347, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 13.12, "pct_cuda_time": 0.023316032848807088, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.895, "pct_cuda_time": 0.06556745670401963, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", "cpu_time_us": 0, "cuda_time_us": 1.248, "pct_cuda_time": 0.0022178665392767714, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 188.643, "cuda_time_us": 129.47, "pct_cuda_time": 0.23008588208346445, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.734, "pct_cuda_time": 0.2287779095090192, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 79.488, "cuda_time_us": 26.304, "pct_cuda_time": 0.04674580244321811, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.304, "pct_cuda_time": 0.04674580244321811, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 453.877, "cuda_time_us": 1301.8709999999999, "pct_cuda_time": 2.3136026677522357, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 154.406, "cuda_time_us": 780.406, "pct_cuda_time": 1.3868881045279071, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.769, "pct_cuda_time": 0.0013666180838973058, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 779.637, "pct_cuda_time": 1.38552148644401, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 100.911, "cuda_time_us": 109.79, "pct_cuda_time": 0.19511183281025385, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.79, "pct_cuda_time": 0.19511183281025385, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 151.109, "cuda_time_us": 411.675, "pct_cuda_time": 0.7316027304140745, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 410.939, "pct_cuda_time": 0.7302947578396293, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2431.241, "cuda_time_us": 1738.282, "pct_cuda_time": 3.0891646503422314, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 65.585, "cuda_time_us": 26.912, "pct_cuda_time": 0.04782630152645551, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.912, "pct_cuda_time": 0.04782630152645551, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1742.482, "cuda_time_us": 380.924, "pct_cuda_time": 0.6769540012880328, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 149.845, "cuda_time_us": 167.13400000000001, "pct_cuda_time": 0.2970199568713814, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.366, "pct_cuda_time": 0.2956551159241342, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 468.053, "cuda_time_us": 34.655, "pct_cuda_time": 0.06158667060788183, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 34.655, "pct_cuda_time": 0.06158667060788183, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 774.958, "cuda_time_us": 50.367999999999995, "pct_cuda_time": 0.08951081879029842, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.736, "pct_cuda_time": 0.02263361237518347, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.32, "pct_cuda_time": 0.06454560313023425, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0023316032848807087, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 207.638, "cuda_time_us": 128.767, "pct_cuda_time": 0.22883655501847122, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.737, "pct_cuda_time": 0.0013097497110953372, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.03, "pct_cuda_time": 0.2275268053073759, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 82.658, "cuda_time_us": 26.08, "pct_cuda_time": 0.04634772383360433, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.08, "pct_cuda_time": 0.04634772383360433, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 473.757, "cuda_time_us": 1304.366, "pct_cuda_time": 2.318036623694139, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 175.162, "cuda_time_us": 781.301, "pct_cuda_time": 1.3884786418297124, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 780.565, "pct_cuda_time": 1.3871706692552672, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 98.531, "cuda_time_us": 110.047, "pct_cuda_time": 0.19556855692931963, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 110.047, "pct_cuda_time": 0.19556855692931963, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 147.816, "cuda_time_us": 413.018, "pct_cuda_time": 0.7339894249351071, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 412.25, "pct_cuda_time": 0.7326245839878599, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2487.437, "cuda_time_us": 1737.1260000000002, "pct_cuda_time": 3.087110280374761, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 70.313, "cuda_time_us": 26.368, "pct_cuda_time": 0.04685953918882205, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.368, "pct_cuda_time": 0.04685953918882205, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1791.537, "cuda_time_us": 380.98400000000004, "pct_cuda_time": 0.6770606294870366, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 166.528, "cuda_time_us": 167.90099999999998, "pct_cuda_time": 0.29838302068197853, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 167.165, "pct_cuda_time": 0.29707504810753327, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 524.056, "cuda_time_us": 33.919, "pct_cuda_time": 0.060278698033436544, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.919, "pct_cuda_time": 0.060278698033436544, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 767.899, "cuda_time_us": 50.526, "pct_cuda_time": 0.08979160638100815, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.64, "pct_cuda_time": 0.02246300725677756, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.383, "pct_cuda_time": 0.06465756273918813, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0026710363850424583, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 193.914, "cuda_time_us": 128.638, "pct_cuda_time": 0.22860730439061328, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 127.87, "pct_cuda_time": 0.22724246344336604, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 78.082, "cuda_time_us": 26.816, "pct_cuda_time": 0.04765569640804961, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.816, "pct_cuda_time": 0.04765569640804961, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 483.276, "cuda_time_us": 1302.958, "pct_cuda_time": 2.315534415290853, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 165.665, "cuda_time_us": 781.109, "pct_cuda_time": 1.3881374315929007, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 780.373, "pct_cuda_time": 1.3868294590184553, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 102.054, "cuda_time_us": 109.759, "pct_cuda_time": 0.1950567415741019, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.759, "pct_cuda_time": 0.1950567415741019, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 164.643, "cuda_time_us": 412.09, "pct_cuda_time": 0.7323402421238501, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 411.354, "pct_cuda_time": 0.7310322695494048, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2475.945, "cuda_time_us": 1735.561, "pct_cuda_time": 3.084329061517414, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 69.04, "cuda_time_us": 25.92, "pct_cuda_time": 0.04606338196959449, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 25.92, "pct_cuda_time": 0.04606338196959449, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1748.299, "cuda_time_us": 379.995, "pct_cuda_time": 0.6753030413401256, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 143.278, "cuda_time_us": 166.749, "pct_cuda_time": 0.2963357592611077, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.013, "pct_cuda_time": 0.2950277866866624, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 507.384, "cuda_time_us": 33.695, "pct_cuda_time": 0.05988061942382277, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.695, "pct_cuda_time": 0.05988061942382277, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 748.294, "cuda_time_us": 50.592, "pct_cuda_time": 0.0899088973999122, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.704, "pct_cuda_time": 0.022576744002381497, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.416, "pct_cuda_time": 0.06471620824864016, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.002615945148890551, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 209.94, "cuda_time_us": 128.959, "pct_cuda_time": 0.22917776525528305, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.191, "pct_cuda_time": 0.22781292430803576, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 93.65, "cuda_time_us": 26.591, "pct_cuda_time": 0.04725584066178577, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.591, "pct_cuda_time": 0.04725584066178577, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 496.974, "cuda_time_us": 1303.0549999999998, "pct_cuda_time": 2.315706797545908, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 183.995, "cuda_time_us": 782.294, "pct_cuda_time": 1.3902433385232233, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 781.558, "pct_cuda_time": 1.3889353659487782, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 100.644, "cuda_time_us": 110.079, "pct_cuda_time": 0.19562542530212162, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 110.079, "pct_cuda_time": 0.19562542530212162, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 158.484, "cuda_time_us": 410.682, "pct_cuda_time": 0.7298380337205636, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 409.946, "pct_cuda_time": 0.7285300611461183, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2802.835, "cuda_time_us": 1736.33, "pct_cuda_time": 3.085695679601312, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 71.349, "cuda_time_us": 25.568, "pct_cuda_time": 0.04543782986877284, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 25.568, "pct_cuda_time": 0.04543782986877284, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 2113.857, "cuda_time_us": 380.188, "pct_cuda_time": 0.6756460287135876, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 173.501, "cuda_time_us": 166.43, "pct_cuda_time": 0.2957688526697381, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 165.662, "pct_cuda_time": 0.2944040117224908, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 516.143, "cuda_time_us": 34.144, "pct_cuda_time": 0.0606785537797004, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 34.144, "pct_cuda_time": 0.0606785537797004, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 1045.023, "cuda_time_us": 50.911, "pct_cuda_time": 0.09047580399128184, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.768, "pct_cuda_time": 0.022690480747985436, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.863, "pct_cuda_time": 0.06551058833121766, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0022747349120787403, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 232.334, "cuda_time_us": 128.703, "pct_cuda_time": 0.2287228182728673, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 127.967, "pct_cuda_time": 0.22741484569842202, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 83.073, "cuda_time_us": 26.815, "pct_cuda_time": 0.047653919271399545, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.815, "pct_cuda_time": 0.047653919271399545, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 463.591, "cuda_time_us": 1303.759, "pct_cuda_time": 2.316957901747552, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 162.31, "cuda_time_us": 781.654, "pct_cuda_time": 1.3891059710671843, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 780.918, "pct_cuda_time": 1.3877979984927389, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 104.568, "cuda_time_us": 109.31, "pct_cuda_time": 0.1942588072182243, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.31, "pct_cuda_time": 0.1942588072182243, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 146.012, "cuda_time_us": 412.79499999999996, "pct_cuda_time": 0.7335931234621433, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 412.027, "pct_cuda_time": 0.7322282825148961, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2366.646, "cuda_time_us": 1735.8519999999999, "pct_cuda_time": 3.084846208282582, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 71.236, "cuda_time_us": 26.239, "pct_cuda_time": 0.046630288560964114, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.239, "pct_cuda_time": 0.046630288560964114, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1696.07, "cuda_time_us": 379.70799999999997, "pct_cuda_time": 0.674793003121558, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 149.163, "cuda_time_us": 166.846, "pct_cuda_time": 0.29650814151616367, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.11, "pct_cuda_time": 0.2952001689417184, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 486.146, "cuda_time_us": 33.887, "pct_cuda_time": 0.060221829660634586, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.887, "pct_cuda_time": 0.060221829660634586, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 726.62, "cuda_time_us": 50.239999999999995, "pct_cuda_time": 0.08928334529909056, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.672, "pct_cuda_time": 0.02251987562957953, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.256, "pct_cuda_time": 0.06443186638463032, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0023316032848807087, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 196.618, "cuda_time_us": 128.73499999999999, "pct_cuda_time": 0.22877968664566922, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 127.999, "pct_cuda_time": 0.22747171407122396, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 77.232, "cuda_time_us": 26.431, "pct_cuda_time": 0.04697149879777593, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.431, "pct_cuda_time": 0.04697149879777593, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 450.273, "cuda_time_us": 1303.474, "pct_cuda_time": 2.316451417802284, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 157.232, "cuda_time_us": 781.366, "pct_cuda_time": 1.3885941557119663, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 780.63, "pct_cuda_time": 1.3872861831375212, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 98.993, "cuda_time_us": 109.567, "pct_cuda_time": 0.19471553133729008, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.567, "pct_cuda_time": 0.19471553133729008, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 144.595, "cuda_time_us": 412.541, "pct_cuda_time": 0.7331417307530278, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.737, "pct_cuda_time": 0.0013097497110953372, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 411.804, "pct_cuda_time": 0.7318319810419324, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2527.597, "cuda_time_us": 1738.694, "pct_cuda_time": 3.0898968306420573, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 73.789, "cuda_time_us": 25.855, "pct_cuda_time": 0.04594786808734049, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 25.855, "pct_cuda_time": 0.04594786808734049, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1815.261, "cuda_time_us": 381.466, "pct_cuda_time": 0.6779172093523662, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 157.713, "cuda_time_us": 167.518, "pct_cuda_time": 0.29770237734500504, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.782, "pct_cuda_time": 0.2963944047705598, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 521.676, "cuda_time_us": 33.407, "pct_cuda_time": 0.059368804068605056, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.407, "pct_cuda_time": 0.059368804068605056, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 763.133, "cuda_time_us": 51.519, "pct_cuda_time": 0.09155630307451923, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.8, "pct_cuda_time": 0.022747349120787404, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 37.215, "pct_cuda_time": 0.06613614043203932, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0026728135216925195, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 224.882, "cuda_time_us": 129.022, "pct_cuda_time": 0.22928972486423688, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.286, "pct_cuda_time": 0.22798175228979164, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 86.104, "cuda_time_us": 25.151, "pct_cuda_time": 0.04469676388569719, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 25.151, "pct_cuda_time": 0.04469676388569719, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 482.007, "cuda_time_us": 1306.222, "pct_cuda_time": 2.3213349893166537, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 172.708, "cuda_time_us": 783.67, "pct_cuda_time": 1.392688678553708, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 782.934, "pct_cuda_time": 1.3913807059792627, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 99.537, "cuda_time_us": 109.534, "pct_cuda_time": 0.1946568858278381, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.534, "pct_cuda_time": 0.1946568858278381, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 156.736, "cuda_time_us": 413.018, "pct_cuda_time": 0.7339894249351071, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 412.282, "pct_cuda_time": 0.7326814523606618, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2448.72, "cuda_time_us": 1734.826, "pct_cuda_time": 3.0830228660796193, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 77.077, "cuda_time_us": 25.696, "pct_cuda_time": 0.045665303359980716, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 25.696, "pct_cuda_time": 0.045665303359980716, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1721.12, "cuda_time_us": 380.31600000000003, "pct_cuda_time": 0.6758735022047956, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 150.15, "cuda_time_us": 167.55, "pct_cuda_time": 0.297759245717807, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.782, "pct_cuda_time": 0.2963944047705598, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 492.687, "cuda_time_us": 33.696, "pct_cuda_time": 0.05988239656047283, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.696, "pct_cuda_time": 0.05988239656047283, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 739.693, "cuda_time_us": 50.207, "pct_cuda_time": 0.08922469978963853, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.672, "pct_cuda_time": 0.02251987562957953, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.063, "pct_cuda_time": 0.06408887901116844, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.002615945148890551, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 200.738, "cuda_time_us": 128.863, "pct_cuda_time": 0.2290071601368771, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.737, "pct_cuda_time": 0.0013097497110953372, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.126, "pct_cuda_time": 0.2276974104257818, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 94.626, "cuda_time_us": 27.136, "pct_cuda_time": 0.04822438013606929, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 27.136, "pct_cuda_time": 0.04822438013606929, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 485.926, "cuda_time_us": 1301.6779999999999, "pct_cuda_time": 2.3132596803787737, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 158.445, "cuda_time_us": 780.821, "pct_cuda_time": 1.387625616237683, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 780.085, "pct_cuda_time": 1.3863176436632378, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 112.472, "cuda_time_us": 109.567, "pct_cuda_time": 0.19471553133729008, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.567, "pct_cuda_time": 0.19471553133729008, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 156.316, "cuda_time_us": 411.28999999999996, "pct_cuda_time": 0.7309185328038007, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 410.554, "pct_cuda_time": 0.7296105602293556, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2484.586, "cuda_time_us": 1741.095, "pct_cuda_time": 3.094163735738855, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 74.43, "cuda_time_us": 26.272, "pct_cuda_time": 0.04668893407041614, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.272, "pct_cuda_time": 0.04668893407041614, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1794.734, "cuda_time_us": 380.73, "pct_cuda_time": 0.676609236777921, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 169.351, "cuda_time_us": 166.845, "pct_cuda_time": 0.2965063643795136, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.077, "pct_cuda_time": 0.29514152343226635, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 513.529, "cuda_time_us": 34.559, "pct_cuda_time": 0.06141606548947592, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 34.559, "pct_cuda_time": 0.06141606548947592, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 758.591, "cuda_time_us": 50.655, "pct_cuda_time": 0.09002085700886608, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.672, "pct_cuda_time": 0.02251987562957953, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.703, "pct_cuda_time": 0.06522624646720782, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0022747349120787403, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 206.485, "cuda_time_us": 128.671, "pct_cuda_time": 0.22866594990006528, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.737, "pct_cuda_time": 0.0013097497110953372, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 127.934, "pct_cuda_time": 0.22735620018896996, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 80.592, "cuda_time_us": 27.104, "pct_cuda_time": 0.04816751176326732, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 27.104, "pct_cuda_time": 0.04816751176326732, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 464.319, "cuda_time_us": 1306.989, "pct_cuda_time": 2.3226980531272505, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 158.488, "cuda_time_us": 782.964, "pct_cuda_time": 1.3914340200787647, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.735, "pct_cuda_time": 0.0013061954377952142, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 782.229, "pct_cuda_time": 1.3901278246409696, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 104.309, "cuda_time_us": 109.726, "pct_cuda_time": 0.19499809606464988, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.726, "pct_cuda_time": 0.19499809606464988, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 148.519, "cuda_time_us": 414.299, "pct_cuda_time": 0.7362659369838359, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 413.531, "pct_cuda_time": 0.7349010960365887, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2429.488, "cuda_time_us": 1734.279, "pct_cuda_time": 3.0820507723320354, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 69.028, "cuda_time_us": 26.816, "pct_cuda_time": 0.04765569640804961, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.816, "pct_cuda_time": 0.04765569640804961, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1741.213, "cuda_time_us": 380.442, "pct_cuda_time": 0.6760974214227032, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 151.652, "cuda_time_us": 167.07, "pct_cuda_time": 0.29690622012577744, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.334, "pct_cuda_time": 0.2955982475513322, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 469.489, "cuda_time_us": 33.887, "pct_cuda_time": 0.060221829660634586, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.887, "pct_cuda_time": 0.060221829660634586, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 783.742, "cuda_time_us": 50.590999999999994, "pct_cuda_time": 0.08990712026326214, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.736, "pct_cuda_time": 0.02263361237518347, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.543, "pct_cuda_time": 0.06494190460319797, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0023316032848807087, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 196.411, "cuda_time_us": 128.89399999999998, "pct_cuda_time": 0.229062251373029, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.158, "pct_cuda_time": 0.22775427879858373, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 79.535, "cuda_time_us": 26.144, "pct_cuda_time": 0.04646146057920827, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.144, "pct_cuda_time": 0.04646146057920827, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 471.335, "cuda_time_us": 1300.877, "pct_cuda_time": 2.3118361939220744, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 162.443, "cuda_time_us": 778.997, "pct_cuda_time": 1.3843841189879706, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 778.261, "pct_cuda_time": 1.3830761464135253, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 97.699, "cuda_time_us": 110.015, "pct_cuda_time": 0.19551168855651765, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 110.015, "pct_cuda_time": 0.19551168855651765, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 158.27, "cuda_time_us": 411.865, "pct_cuda_time": 0.7319403863775863, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.735, "pct_cuda_time": 0.0013061954377952142, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 411.13, "pct_cuda_time": 0.730634190939791, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2403.725, "cuda_time_us": 1738.6009999999999, "pct_cuda_time": 3.0897315569336015, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 69.672, "cuda_time_us": 25.824, "pct_cuda_time": 0.04589277685118859, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 25.824, "pct_cuda_time": 0.04589277685118859, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1704.497, "cuda_time_us": 381.81899999999996, "pct_cuda_time": 0.6785445385898379, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 142.936, "cuda_time_us": 167.998, "pct_cuda_time": 0.29855540293703453, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 167.262, "pct_cuda_time": 0.29724743036258927, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 460.095, "cuda_time_us": 34.047, "pct_cuda_time": 0.06050617152464443, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 34.047, "pct_cuda_time": 0.06050617152464443, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 753.805, "cuda_time_us": 50.88, "pct_cuda_time": 0.09042071275512993, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.704, "pct_cuda_time": 0.022576744002381497, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.703, "pct_cuda_time": 0.06522624646720782, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.002617722285540613, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 202.098, "cuda_time_us": 128.89399999999998, "pct_cuda_time": 0.229062251373029, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.158, "pct_cuda_time": 0.22775427879858373, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 90.814, "cuda_time_us": 26.784, "pct_cuda_time": 0.047598828035247634, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.784, "pct_cuda_time": 0.047598828035247634, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 470.218, "cuda_time_us": 1304.174, "pct_cuda_time": 2.317695413457327, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 165.921, "cuda_time_us": 782.293, "pct_cuda_time": 1.3902415613865733, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 781.557, "pct_cuda_time": 1.3889335888121281, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 97.595, "cuda_time_us": 109.695, "pct_cuda_time": 0.19494300482849797, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.695, "pct_cuda_time": 0.19494300482849797, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 155.654, "cuda_time_us": 412.186, "pct_cuda_time": 0.732510847242256, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 411.45, "pct_cuda_time": 0.7312028746678106, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2702.258, "cuda_time_us": 1737.959, "pct_cuda_time": 3.088590635204262, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 72.128, "cuda_time_us": 26.24, "pct_cuda_time": 0.046632065697614175, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.24, "pct_cuda_time": 0.046632065697614175, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 2005.119, "cuda_time_us": 380.888, "pct_cuda_time": 0.6768900243686305, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 154.048, "cuda_time_us": 167.38899999999998, "pct_cuda_time": 0.29747312671714704, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.653, "pct_cuda_time": 0.2961651541427018, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 528.646, "cuda_time_us": 34.207, "pct_cuda_time": 0.06079051338865427, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 34.207, "pct_cuda_time": 0.06079051338865427, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 949.686, "cuda_time_us": 50.59, "pct_cuda_time": 0.0899053431266121, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.703, "pct_cuda_time": 0.022574966865731436, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.447, "pct_cuda_time": 0.06477129948479207, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0025590767760885827, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 210.179, "cuda_time_us": 128.702, "pct_cuda_time": 0.22872104113621722, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 127.966, "pct_cuda_time": 0.22741306856177193, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 83.845, "cuda_time_us": 26.72, "pct_cuda_time": 0.047485091289643705, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.72, "pct_cuda_time": 0.047485091289643705, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 471.681, "cuda_time_us": 1304.111, "pct_cuda_time": 2.3175834538483735, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 159.371, "cuda_time_us": 781.717, "pct_cuda_time": 1.3892179306761379, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 780.981, "pct_cuda_time": 1.3879099581016927, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 104.257, "cuda_time_us": 109.791, "pct_cuda_time": 0.19511360994690388, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.791, "pct_cuda_time": 0.19511360994690388, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 156.288, "cuda_time_us": 412.603, "pct_cuda_time": 0.7332519132253317, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 411.867, "pct_cuda_time": 0.7319439406508863, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2428.616, "cuda_time_us": 1739.6219999999998, "pct_cuda_time": 3.091546013453314, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 68.749, "cuda_time_us": 26.079, "pct_cuda_time": 0.04634594669695427, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.079, "pct_cuda_time": 0.04634594669695427, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1755.635, "cuda_time_us": 382.491, "pct_cuda_time": 0.6797387744186792, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 149.79, "cuda_time_us": 168.06199999999998, "pct_cuda_time": 0.2986691396826384, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 167.326, "pct_cuda_time": 0.2973611671081932, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 516.584, "cuda_time_us": 33.663, "pct_cuda_time": 0.05982375105102081, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.663, "pct_cuda_time": 0.05982375105102081, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 754.985, "cuda_time_us": 50.912, "pct_cuda_time": 0.09047758112793189, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.768, "pct_cuda_time": 0.022690480747985436, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.64, "pct_cuda_time": 0.06511428685825393, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0026728135216925195, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 193.582, "cuda_time_us": 129.85399999999998, "pct_cuda_time": 0.23076830255708805, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 129.118, "pct_cuda_time": 0.22946032998264282, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 77.065, "cuda_time_us": 26.079, "pct_cuda_time": 0.04634594669695427, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.079, "pct_cuda_time": 0.04634594669695427, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 462.299, "cuda_time_us": 1304.973, "pct_cuda_time": 2.3191153456407267, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 161.388, "cuda_time_us": 782.741, "pct_cuda_time": 1.391037718605801, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 782.005, "pct_cuda_time": 1.3897297460313558, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 100.718, "cuda_time_us": 109.503, "pct_cuda_time": 0.19460179459168617, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.503, "pct_cuda_time": 0.19460179459168617, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 146.296, "cuda_time_us": 412.729, "pct_cuda_time": 0.7334758324432393, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.767, "pct_cuda_time": 0.0013630638105971826, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 411.962, "pct_cuda_time": 0.7321127686326422, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2360.475, "cuda_time_us": 1734.6000000000001, "pct_cuda_time": 3.0826212331967056, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 69.367, "cuda_time_us": 26.207, "pct_cuda_time": 0.04657342018816215, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.207, "pct_cuda_time": 0.04657342018816215, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1681.601, "cuda_time_us": 380.221, "pct_cuda_time": 0.6757046742230396, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 153.016, "cuda_time_us": 166.879, "pct_cuda_time": 0.2965667870256157, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.737, "pct_cuda_time": 0.0013097497110953372, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.142, "pct_cuda_time": 0.29525703731452035, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 502.198, "cuda_time_us": 33.76, "pct_cuda_time": 0.059996133306076775, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.76, "pct_cuda_time": 0.059996133306076775, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 711.404, "cuda_time_us": 50.206999999999994, "pct_cuda_time": 0.08922469978963851, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.703, "pct_cuda_time": 0.022574966865731436, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.224, "pct_cuda_time": 0.06437499801182835, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0022747349120787403, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 186.759, "cuda_time_us": 129.375, "pct_cuda_time": 0.22991705410170862, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.639, "pct_cuda_time": 0.22860908152726334, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 72.998, "cuda_time_us": 26.463, "pct_cuda_time": 0.047028367170577894, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.463, "pct_cuda_time": 0.047028367170577894, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 472.439, "cuda_time_us": 1301.709, "pct_cuda_time": 2.313314771614926, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 158.147, "cuda_time_us": 780.148, "pct_cuda_time": 1.3864296032721914, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.735, "pct_cuda_time": 0.0013061954377952142, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 779.413, "pct_cuda_time": 1.3851234078343961, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 91.763, "cuda_time_us": 109.695, "pct_cuda_time": 0.19494300482849797, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.695, "pct_cuda_time": 0.19494300482849797, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 176.843, "cuda_time_us": 411.866, "pct_cuda_time": 0.7319421635142362, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 411.13, "pct_cuda_time": 0.730634190939791, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2569.828, "cuda_time_us": 1742.0579999999998, "pct_cuda_time": 3.095875118332864, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 69.638, "cuda_time_us": 25.76, "pct_cuda_time": 0.045779040105584645, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 25.76, "pct_cuda_time": 0.045779040105584645, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1872.215, "cuda_time_us": 381.11699999999996, "pct_cuda_time": 0.6772969886614947, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 148.007, "cuda_time_us": 166.142, "pct_cuda_time": 0.29525703731452035, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 165.406, "pct_cuda_time": 0.2939490647400751, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 521.055, "cuda_time_us": 34.368, "pct_cuda_time": 0.06107663238931418, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 34.368, "pct_cuda_time": 0.06107663238931418, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 861.87, "cuda_time_us": 51.424, "pct_cuda_time": 0.09138747509276339, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 13.247, "pct_cuda_time": 0.0235417292033649, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.608, "pct_cuda_time": 0.06505741848545198, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", "cpu_time_us": 0, "cuda_time_us": 1.569, "pct_cuda_time": 0.0027883274039465183, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 200.055, "cuda_time_us": 129.183, "pct_cuda_time": 0.2295758438648968, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.447, "pct_cuda_time": 0.2282678712904515, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 86.13, "cuda_time_us": 27.135, "pct_cuda_time": 0.04822260299941923, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 27.135, "pct_cuda_time": 0.04822260299941923, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 474.521, "cuda_time_us": 1308.0459999999998, "pct_cuda_time": 2.3245764865663654, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 168.657, "cuda_time_us": 783.5409999999999, "pct_cuda_time": 1.39245942792585, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 782.805, "pct_cuda_time": 1.3911514553514048, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 97.594, "cuda_time_us": 109.823, "pct_cuda_time": 0.19517047831970585, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.823, "pct_cuda_time": 0.19517047831970585, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 153.334, "cuda_time_us": 414.68199999999996, "pct_cuda_time": 0.7369465803208094, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 413.914, "pct_cuda_time": 0.7355817393735623, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2418.348, "cuda_time_us": 1734.4740000000002, "pct_cuda_time": 3.082397313978798, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 70.13, "cuda_time_us": 26.144, "pct_cuda_time": 0.04646146057920827, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.144, "pct_cuda_time": 0.04646146057920827, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1735.792, "cuda_time_us": 380.731, "pct_cuda_time": 0.676611013914571, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 156.604, "cuda_time_us": 167.45399999999998, "pct_cuda_time": 0.29758864059940104, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.718, "pct_cuda_time": 0.2962806680249558, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 508.154, "cuda_time_us": 33.344, "pct_cuda_time": 0.05925684445965119, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.344, "pct_cuda_time": 0.05925684445965119, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 732.914, "cuda_time_us": 50.623000000000005, "pct_cuda_time": 0.08996398863606413, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.768, "pct_cuda_time": 0.022690480747985436, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.383, "pct_cuda_time": 0.06465756273918813, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.002615945148890551, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 190.787, "cuda_time_us": 129.31, "pct_cuda_time": 0.22980154021945465, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.768, "pct_cuda_time": 0.0013648409472472442, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 128.542, "pct_cuda_time": 0.22843669927220736, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 94.393, "cuda_time_us": 26.112, "pct_cuda_time": 0.046404592206406296, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.112, "pct_cuda_time": 0.046404592206406296, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 452.942, "cuda_time_us": 1301.487, "pct_cuda_time": 2.312920247278612, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 156.587, "cuda_time_us": 779.573, "pct_cuda_time": 1.385407749698406, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 778.837, "pct_cuda_time": 1.3840997771239607, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 101.349, "cuda_time_us": 109.919, "pct_cuda_time": 0.19534108343811177, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.919, "pct_cuda_time": 0.19534108343811177, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 144.714, "cuda_time_us": 411.995, "pct_cuda_time": 0.7321714141420943, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.737, "pct_cuda_time": 0.0013097497110953372, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 411.258, "pct_cuda_time": 0.7308616644309989, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2479.5, "cuda_time_us": 1737.2240000000002, "pct_cuda_time": 3.087284439766467, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 65.657, "cuda_time_us": 25.792, "pct_cuda_time": 0.045835908478386624, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 25.792, "pct_cuda_time": 0.045835908478386624, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1734.516, "cuda_time_us": 381.435, "pct_cuda_time": 0.6778621181162143, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 148.148, "cuda_time_us": 166.815, "pct_cuda_time": 0.29645305028001173, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 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": 166.079, "pct_cuda_time": 0.2951450777055665, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[2560, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 484.754, "cuda_time_us": 33.791, "pct_cuda_time": 0.06005122454222868, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 33.791, "pct_cuda_time": 0.06005122454222868, "trace": "_C::rotary_embedding(int64[2560], bfloat16[2560, 4096], bfloat16[2560, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 743.297, "cuda_time_us": 50.847, "pct_cuda_time": 0.0903620672456779, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 12.96, "pct_cuda_time": 0.023031690984797244, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[2560], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 36.607, "pct_cuda_time": 0.0650556413488019, "trace": "_vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.0022747349120787403, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], None, None, bfloat16[2560, 32, 128], int32[21], int32[21], 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[2560, 32, 128], bfloat16[2560, 8, 128], bfloat16[2560, 8, 128], bfloat16[2560, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 219.114, "cuda_time_us": 129.982, "pct_cuda_time": 0.23099577604829596, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 129.246, "pct_cuda_time": 0.22968780347385068, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[2560, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 116.971, "cuda_time_us": 26.559, "pct_cuda_time": 0.0471989722889838, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 26.559, "pct_cuda_time": 0.0471989722889838, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 489.506, "cuda_time_us": 1303.438, "pct_cuda_time": 2.3163874408828824, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 174.767, "cuda_time_us": 782.005, "pct_cuda_time": 1.3897297460313558, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 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": 781.269, "pct_cuda_time": 1.3884217734569104, "trace": "mm(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[2560, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[2560, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 106.887, "cuda_time_us": 109.567, "pct_cuda_time": 0.19471553133729008, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 109.567, "pct_cuda_time": 0.19471553133729008, "trace": "_C::silu_and_mul(bfloat16[2560, 14336], bfloat16[2560, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 152.72, "cuda_time_us": 411.866, "pct_cuda_time": 0.7319421635142362, "trace": "" }, "children": [ { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 411.13, "pct_cuda_time": 0.730634190939791, "trace": "mm(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[2560, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[2560, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 77.909, "cuda_time_us": 25.792, "pct_cuda_time": 0.045835908478386624, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 25.792, "pct_cuda_time": 0.045835908478386624, "trace": "_C::fused_add_rms_norm(bfloat16[2560, 4096], bfloat16[2560, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] } ] }, { "entry": { "name": "LogitsProcessor", "cpu_time_us": 547.992, "cuda_time_us": 396.73, "pct_cuda_time": 0.7050434231789052, "trace": "" }, "children": [ { "entry": { "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", "cpu_time_us": 0, "cuda_time_us": 3.936, "pct_cuda_time": 0.006994809854642127, "trace": "index_select(bfloat16[2560, 4096], 0, int64[20])" }, "children": [] }, { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[20, 4096], bfloat16[128256, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 392.058, "pct_cuda_time": 0.6967406407498178, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[20, 4096], bfloat16[128256, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Sampler", "cpu_time_us": 4691.088, "cuda_time_us": 138.428, "pct_cuda_time": 0.2460054721947155, "trace": "" }, "children": [ { "entry": { "name": "Memcpy HtoD (Pinned -> Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.0013079725744452755, "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 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": 0.0013079725744452755, "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 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": 0.0014217093200492128, "trace": "copy_(int32[20], int32[20], True) <- _to_copy(int32[20], 3, 0, None, None, True, None) <- to(int32[20], 3, 0, None, None, True, False, None)" }, "children": [] }, { "entry": { "name": "Memcpy HtoD (Pinned -> Device)", "cpu_time_us": 0, "cuda_time_us": 0.8, "pct_cuda_time": 0.0014217093200492128, "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 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": 0.0013630638105971826, "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 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": 0.0014217093200492128, "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 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": 0.0014217093200492128, "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 15, 0, None, None, True, False, None)" }, "children": [] }, { "entry": { "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", "cpu_time_us": 0, "cuda_time_us": 9.28, "pct_cuda_time": 0.016491828112570866, "trace": "copy_(float32[20, 128256], bfloat16[20, 128256], False) <- _to_copy(bfloat16[20, 128256], 6, None, None, None, False, None) <- to(bfloat16[20, 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": 13.216, "pct_cuda_time": 0.02348663796721299, "trace": "div_(float32[20, 128256], bfloat16[20, 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": 35.391, "pct_cuda_time": 0.06289464318232711, "trace": "_softmax(float32[20, 128256], -1, False) <- softmax(float32[20, 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": 29.407, "pct_cuda_time": 0.052260257468358995, "trace": "_log_softmax(float32[20, 128256], -1, False) <- log_softmax(float32[20, 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": 2.08, "pct_cuda_time": 0.0036964442321279533, "trace": "copy_(int64[20], int32[20], False) <- _to_copy(int32[20], 4, None, None, None, False, None) <- to(int32[20], 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": 12.831, "pct_cuda_time": 0.022802440356939308, "trace": "index(float32[20, 128256], None)" }, "children": [] }, { "entry": { "name": "void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, long, 4> >(at::native::ReduceOp, unsigned int, long, 4>)", "cpu_time_us": 0, "cuda_time_us": 28.0, "pct_cuda_time": 0.049759826201722444, "trace": "argmax(float32[20, 128256], -1, False)" }, "children": [] }, { "entry": { "name": "Memcpy DtoH (Device -> Pageable)", "cpu_time_us": 0, "cuda_time_us": 2.784, "pct_cuda_time": 0.00494754843377126, "trace": "copy_(int64[20], int64[20], False) <- _to_copy(int64[20], 4, 0, None, None, False, None) <- to(int64[20], 4, 0, None, None, False, False, None)" }, "children": [] } ] } ] }, "decode_1": { "metadata": { "num_running_seqs": 20 }, "summary_stats": [ { "entry": { "name": "LlamaForCausalLM", "cuda_time_us": 6754.447000000001, "pct_cuda_time": 92.73999863796283, "invocations": 1 }, "children": [ { "entry": { "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", "cuda_time_us": 3.488, "pct_cuda_time": 0.04789098430252163, "invocations": 1 }, "children": [ { "entry": { "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", "cuda_time_us": 3.488, "pct_cuda_time": 0.04789098430252163, "invocations": 1 }, "children": [] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cuda_time_us": 6747.855, "pct_cuda_time": 92.64948907129933, "invocations": 32 }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cuda_time_us": 198.366, "pct_cuda_time": 2.723607509218465, "invocations": 64 }, "children": [ { "entry": { "name": "void vllm::rms_norm_kernel(c10::BFloat16*, c10::BFloat16 const*, c10::BFloat16 const*, float, int, int)", "cuda_time_us": 4.48, "pct_cuda_time": 0.061511355984890166, "invocations": 1 }, "children": [] }, { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cuda_time_us": 193.886, "pct_cuda_time": 2.6620961532335743, "invocations": 63 }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cuda_time_us": 2054.477, "pct_cuda_time": 28.208407613787763, "invocations": 32 }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cuda_time_us": 721.622, "pct_cuda_time": 9.908024046546519, "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": 721.622, "pct_cuda_time": 9.908024046546519, "invocations": 32 }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cuda_time_us": 119.233, "pct_cuda_time": 1.637094533068395, "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": 119.233, "pct_cuda_time": 1.637094533068395, "invocations": 32 }, "children": [] } ] }, { "entry": { "name": "Attention", "cuda_time_us": 685.7250000000001, "pct_cuda_time": 9.4151505765042, "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": 80.995, "pct_cuda_time": 1.11207863348129, "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": 562.618, "pct_cuda_time": 7.724865196764941, "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": 42.111999999999995, "pct_cuda_time": 0.5782067462579674, "invocations": 32 }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cuda_time_us": 527.8969999999999, "pct_cuda_time": 7.248138457668651, "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": 527.8969999999999, "pct_cuda_time": 7.248138457668651, "invocations": 32 }, "children": [] } ] } ] }, { "entry": { "name": "LlamaMLP", "cuda_time_us": 4495.012000000001, "pct_cuda_time": 61.71747394829311, "invocations": 32 }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cuda_time_us": 2709.624, "pct_cuda_time": 37.2037157252683, "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": 2709.624, "pct_cuda_time": 37.2037157252683, "invocations": 32 }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cuda_time_us": 292.03000000000003, "pct_cuda_time": 4.009634216131134, "invocations": 32 }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cuda_time_us": 292.03000000000003, "pct_cuda_time": 4.009634216131134, "invocations": 32 }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cuda_time_us": 1493.358, "pct_cuda_time": 20.50412400689366, "invocations": 32 }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cuda_time_us": 1414.609, "pct_cuda_time": 19.422883432685154, "invocations": 32 }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cuda_time_us": 78.74900000000002, "pct_cuda_time": 1.081240574208508, "invocations": 32 }, "children": [] } ] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cuda_time_us": 3.104, "pct_cuda_time": 0.04261858236095961, "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": 3.104, "pct_cuda_time": 0.04261858236095961, "invocations": 1 }, "children": [] } ] } ] }, { "entry": { "name": "LogitsProcessor", "cuda_time_us": 389.915, "pct_cuda_time": 5.3536161537608145, "invocations": 1 }, "children": [ { "entry": { "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", "cuda_time_us": 3.232, "pct_cuda_time": 0.04437604967481362, "invocations": 1 }, "children": [] }, { "entry": { "name": "Memset (Device)", "cuda_time_us": 0.736, "pct_cuda_time": 0.010105437054660526, "invocations": 1 }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", "cuda_time_us": 385.947, "pct_cuda_time": 5.29913466703134, "invocations": 1 }, "children": [] } ] }, { "entry": { "name": "Sampler", "cuda_time_us": 138.846, "pct_cuda_time": 1.9063852082763528, "invocations": 1 }, "children": [ { "entry": { "name": "Memcpy HtoD (Pinned -> Device)", "cuda_time_us": 5.342999999999999, "pct_cuda_time": 0.07336053014001519, "invocations": 7 }, "children": [] }, { "entry": { "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", "cuda_time_us": 9.28, "pct_cuda_time": 0.12741638025441535, "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": 13.216, "pct_cuda_time": 0.18145850015542597, "invocations": 1 }, "children": [] }, { "entry": { "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::SoftMaxForwardEpilogue>(float*, float const*, int)", "cuda_time_us": 35.392, "pct_cuda_time": 0.4859397122806323, "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": 29.791, "pct_cuda_time": 0.40903678708613006, "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": 2.112, "pct_cuda_time": 0.02899821067859108, "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": 12.992, "pct_cuda_time": 0.1783829323561815, "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": 28.223, "pct_cuda_time": 0.3875078124914185, "invocations": 1 }, "children": [] }, { "entry": { "name": "Memcpy DtoH (Device -> Pageable)", "cuda_time_us": 2.497, "pct_cuda_time": 0.03428434283354257, "invocations": 1 }, "children": [] } ] } ], "model_stats": [ { "entry": { "name": "LlamaForCausalLM", "cpu_time_us": 96341.781, "cuda_time_us": 6754.447000000001, "pct_cuda_time": 92.73999863796283, "trace": "" }, "children": [ { "entry": { "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", "cpu_time_us": 385.689, "cuda_time_us": 3.488, "pct_cuda_time": 0.04789098430252163, "trace": "" }, "children": [ { "entry": { "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", "cpu_time_us": 0, "cuda_time_us": 3.488, "pct_cuda_time": 0.04789098430252163, "trace": "index_select(bfloat16[128256, 4096], 0, int64[20]) <- embedding(bfloat16[128256, 4096], int64[20], -1, False, False)" }, "children": [] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 11236.205, "cuda_time_us": 219.64499999999998, "pct_cuda_time": 3.0157727199333033, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 319.938, "cuda_time_us": 4.48, "pct_cuda_time": 0.061511355984890166, "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": 4.48, "pct_cuda_time": 0.061511355984890166, "trace": "_C::rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 9718.282, "cuda_time_us": 72.15899999999999, "pct_cuda_time": 0.9907584679718054, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 741.019, "cuda_time_us": 28.159, "pct_cuda_time": 0.3866290788344915, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 28.159, "pct_cuda_time": 0.3866290788344915, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 1444.956, "cuda_time_us": 3.584, "pct_cuda_time": 0.04920908478791213, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 3.584, "pct_cuda_time": 0.04920908478791213, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 1589.362, "cuda_time_us": 22.304, "pct_cuda_time": 0.3062386794390603, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 2.432, "pct_cuda_time": 0.03339187896322609, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 18.496, "pct_cuda_time": 0.25395402685190366, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.018892773623930548, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 542.896, "cuda_time_us": 18.112, "pct_cuda_time": 0.24868162491034165, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 18.112, "pct_cuda_time": 0.24868162491034165, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 181.062, "cuda_time_us": 2.816, "pct_cuda_time": 0.0386642809047881, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 2.816, "pct_cuda_time": 0.0386642809047881, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 871.656, "cuda_time_us": 140.19, "pct_cuda_time": 1.9248386150718195, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 299.72, "cuda_time_us": 84.383, "pct_cuda_time": 1.158596596444863, "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": 84.383, "pct_cuda_time": 1.158596596444863, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 198.741, "cuda_time_us": 9.184, "pct_cuda_time": 0.12609827976902482, "trace": "" }, "children": [ { "entry": { "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": 0.12609827976902482, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 275.032, "cuda_time_us": 46.623000000000005, "pct_cuda_time": 0.6401437388579317, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.383, "pct_cuda_time": 0.6093880608654867, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.24, "pct_cuda_time": 0.030755677992445083, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2975.749, "cuda_time_us": 212.096, "pct_cuda_time": 2.9121233390560857, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 126.623, "cuda_time_us": 3.104, "pct_cuda_time": 0.04261858236095961, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04261858236095961, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 2138.408, "cuda_time_us": 63.104, "pct_cuda_time": 0.8664313857300243, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 168.575, "cuda_time_us": 21.792, "pct_cuda_time": 0.2992088101836443, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 21.792, "pct_cuda_time": 0.2992088101836443, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 581.397, "cuda_time_us": 3.776, "pct_cuda_time": 0.051845285758693134, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.051845285758693134, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 957.109, "cuda_time_us": 21.215999999999998, "pct_cuda_time": 0.29130020727130124, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 2.464, "pct_cuda_time": 0.033831245791689585, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.439, "pct_cuda_time": 0.23944119129921865, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01802777018039303, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 201.281, "cuda_time_us": 16.32, "pct_cuda_time": 0.2240770825163856, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.32, "pct_cuda_time": 0.2240770825163856, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 106.417, "cuda_time_us": 2.848, "pct_cuda_time": 0.039103647733251604, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 2.848, "pct_cuda_time": 0.039103647733251604, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 505.961, "cuda_time_us": 143.04, "pct_cuda_time": 1.9639697232318503, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 148.898, "cuda_time_us": 87.679, "pct_cuda_time": 1.2038513797766037, "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": 87.679, "pct_cuda_time": 1.2038513797766037, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 124.629, "cuda_time_us": 8.896, "pct_cuda_time": 0.12214397831285333, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 8.896, "pct_cuda_time": 0.12214397831285333, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 154.436, "cuda_time_us": 46.464999999999996, "pct_cuda_time": 0.6379743651423931, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.032, "pct_cuda_time": 0.6045687559657775, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.433, "pct_cuda_time": 0.03340560917661557, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2677.831, "cuda_time_us": 211.195, "pct_cuda_time": 2.8997524167921602, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 71.878, "cuda_time_us": 3.136, "pct_cuda_time": 0.043057949189423114, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.043057949189423114, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1938.302, "cuda_time_us": 64.50999999999999, "pct_cuda_time": 0.8857360657556392, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 163.239, "cuda_time_us": 21.791, "pct_cuda_time": 0.29919507997025485, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 21.791, "pct_cuda_time": 0.29919507997025485, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 541.895, "cuda_time_us": 3.744, "pct_cuda_time": 0.05140591893022964, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05140591893022964, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 848.764, "cuda_time_us": 22.208, "pct_cuda_time": 0.3049205789536698, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 2.464, "pct_cuda_time": 0.033831245791689585, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 18.368, "pct_cuda_time": 0.25219655953804965, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.018892773623930548, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 195.55, "cuda_time_us": 16.767, "pct_cuda_time": 0.23021448790148513, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.767, "pct_cuda_time": 0.23021448790148513, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 81.964, "cuda_time_us": 3.008, "pct_cuda_time": 0.04130048187556911, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04130048187556911, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 489.266, "cuda_time_us": 140.541, "pct_cuda_time": 1.9296579199715287, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 165.936, "cuda_time_us": 84.638, "pct_cuda_time": 1.1620978008591818, "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": 84.638, "pct_cuda_time": 1.1620978008591818, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 97.101, "cuda_time_us": 9.216, "pct_cuda_time": 0.1265376465974883, "trace": "" }, "children": [ { "entry": { "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": 0.1265376465974883, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 168.468, "cuda_time_us": 46.687000000000005, "pct_cuda_time": 0.6410224725148588, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.255, "pct_cuda_time": 0.6076305935516326, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.432, "pct_cuda_time": 0.03339187896322609, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2749.335, "cuda_time_us": 211.233, "pct_cuda_time": 2.900274164900961, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 76.817, "cuda_time_us": 3.105, "pct_cuda_time": 0.042632312574349095, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.042632312574349095, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1976.84, "cuda_time_us": 64.416, "pct_cuda_time": 0.8844454256970278, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 181.32, "cuda_time_us": 22.816, "pct_cuda_time": 0.31326854869447635, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.816, "pct_cuda_time": 0.31326854869447635, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 512.233, "cuda_time_us": 3.616, "pct_cuda_time": 0.049648451616375634, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 3.616, "pct_cuda_time": 0.049648451616375634, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 859.784, "cuda_time_us": 21.568, "pct_cuda_time": 0.2961332423843998, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03427061262015309, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.792, "pct_cuda_time": 0.24428795662570665, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.017574673138540046, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 222.421, "cuda_time_us": 16.416, "pct_cuda_time": 0.22539518300177608, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.416, "pct_cuda_time": 0.22539518300177608, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 91.848, "cuda_time_us": 3.008, "pct_cuda_time": 0.04130048187556911, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04130048187556911, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 510.01, "cuda_time_us": 140.704, "pct_cuda_time": 1.9318959447540147, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 176.85, "cuda_time_us": 85.023, "pct_cuda_time": 1.1673839330141331, "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": 85.023, "pct_cuda_time": 1.1673839330141331, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 100.857, "cuda_time_us": 8.961, "pct_cuda_time": 0.12303644218316981, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 8.961, "pct_cuda_time": 0.12303644218316981, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 167.464, "cuda_time_us": 46.72, "pct_cuda_time": 0.6414755695567117, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.32, "pct_cuda_time": 0.6085230574219491, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.4, "pct_cuda_time": 0.032952512134762586, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2704.155, "cuda_time_us": 209.534, "pct_cuda_time": 2.8769465323522265, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 80.553, "cuda_time_us": 3.104, "pct_cuda_time": 0.04261858236095961, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04261858236095961, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1934.864, "cuda_time_us": 64.128, "pct_cuda_time": 0.8804911242408564, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 172.735, "cuda_time_us": 22.016, "pct_cuda_time": 0.30228437798288876, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.016, "pct_cuda_time": 0.30228437798288876, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 501.397, "cuda_time_us": 3.712, "pct_cuda_time": 0.050966552101766135, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.050966552101766135, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 867.429, "cuda_time_us": 21.472, "pct_cuda_time": 0.2948151418990093, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 2.592, "pct_cuda_time": 0.035588713105543596, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.6, "pct_cuda_time": 0.24165175565492567, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.017574673138540046, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 199.474, "cuda_time_us": 16.928, "pct_cuda_time": 0.23242505225719215, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.928, "pct_cuda_time": 0.23242505225719215, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 81.214, "cuda_time_us": 3.008, "pct_cuda_time": 0.04130048187556911, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04130048187556911, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 527.417, "cuda_time_us": 139.294, "pct_cuda_time": 1.912536343874842, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 174.931, "cuda_time_us": 84.382, "pct_cuda_time": 1.1585828662314737, "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": 84.382, "pct_cuda_time": 1.1585828662314737, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 127.192, "cuda_time_us": 9.056, "pct_cuda_time": 0.12434081245517083, "trace": "" }, "children": [ { "entry": { "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": 0.12434081245517083, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 165.333, "cuda_time_us": 45.856, "pct_cuda_time": 0.6296126651881973, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 43.456, "pct_cuda_time": 0.5966601530534346, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.4, "pct_cuda_time": 0.032952512134762586, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2889.659, "cuda_time_us": 211.902, "pct_cuda_time": 2.9094596776585253, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 73.462, "cuda_time_us": 3.136, "pct_cuda_time": 0.043057949189423114, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.043057949189423114, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 2103.081, "cuda_time_us": 64.863, "pct_cuda_time": 0.8905828310821274, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 146.407, "cuda_time_us": 23.391, "pct_cuda_time": 0.32116342139342985, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 23.391, "pct_cuda_time": 0.32116342139342985, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 496.497, "cuda_time_us": 3.84, "pct_cuda_time": 0.05272401941562014, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05272401941562014, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 1050.905, "cuda_time_us": 21.312, "pct_cuda_time": 0.2926183077566918, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 2.592, "pct_cuda_time": 0.035588713105543596, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.408, "pct_cuda_time": 0.23901555468414465, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 214.43, "cuda_time_us": 16.32, "pct_cuda_time": 0.2240770825163856, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.32, "pct_cuda_time": 0.2240770825163856, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 85.185, "cuda_time_us": 3.008, "pct_cuda_time": 0.04130048187556911, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04130048187556911, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 540.021, "cuda_time_us": 140.89499999999998, "pct_cuda_time": 1.934518415511406, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 177.423, "cuda_time_us": 84.767, "pct_cuda_time": 1.1638689983864252, "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": 84.767, "pct_cuda_time": 1.1638689983864252, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 132.062, "cuda_time_us": 9.408, "pct_cuda_time": 0.12917384756826933, "trace": "" }, "children": [ { "entry": { "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": 0.12917384756826933, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 169.76, "cuda_time_us": 46.72, "pct_cuda_time": 0.6414755695567117, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.32, "pct_cuda_time": 0.6085230574219491, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.4, "pct_cuda_time": 0.032952512134762586, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2666.512, "cuda_time_us": 210.84300000000002, "pct_cuda_time": 2.894919381679062, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 75.58, "cuda_time_us": 3.296, "pct_cuda_time": 0.045254783331740614, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045254783331740614, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1914.455, "cuda_time_us": 63.614000000000004, "pct_cuda_time": 0.8734337945586614, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 166.366, "cuda_time_us": 21.983, "pct_cuda_time": 0.30183128094103584, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 21.983, "pct_cuda_time": 0.30183128094103584, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 547.268, "cuda_time_us": 3.776, "pct_cuda_time": 0.051845285758693134, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.051845285758693134, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 830.045, "cuda_time_us": 21.408, "pct_cuda_time": 0.2939364082420823, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03514934627708009, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.536, "pct_cuda_time": 0.24077302199799863, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 201.056, "cuda_time_us": 16.447, "pct_cuda_time": 0.2258208196168501, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.447, "pct_cuda_time": 0.2258208196168501, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 102.904, "cuda_time_us": 3.008, "pct_cuda_time": 0.04130048187556911, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04130048187556911, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 496.246, "cuda_time_us": 140.925, "pct_cuda_time": 1.934930321913091, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 178.583, "cuda_time_us": 85.055, "pct_cuda_time": 1.1678232998425968, "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": 85.055, "pct_cuda_time": 1.1678232998425968, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 101.206, "cuda_time_us": 9.503, "pct_cuda_time": 0.13047821784027036, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 9.503, "pct_cuda_time": 0.13047821784027036, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 165.002, "cuda_time_us": 46.367000000000004, "pct_cuda_time": 0.6366288042302237, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 43.904, "pct_cuda_time": 0.6028112886519236, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.463, "pct_cuda_time": 0.0338175155783001, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2552.352, "cuda_time_us": 210.334, "pct_cuda_time": 2.887930703063814, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 76.203, "cuda_time_us": 3.072, "pct_cuda_time": 0.04217921553249611, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04217921553249611, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1823.323, "cuda_time_us": 64.384, "pct_cuda_time": 0.8840060588685644, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 161.932, "cuda_time_us": 22.912, "pct_cuda_time": 0.31458664917986684, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.912, "pct_cuda_time": 0.31458664917986684, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 497.497, "cuda_time_us": 3.776, "pct_cuda_time": 0.051845285758693134, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.051845285758693134, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 830.861, "cuda_time_us": 21.408, "pct_cuda_time": 0.2939364082420823, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 2.464, "pct_cuda_time": 0.033831245791689585, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.632, "pct_cuda_time": 0.24209112248338915, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 170.162, "cuda_time_us": 16.288, "pct_cuda_time": 0.2236377156879221, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.288, "pct_cuda_time": 0.2236377156879221, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 75.106, "cuda_time_us": 2.975, "pct_cuda_time": 0.040847384833716124, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 2.975, "pct_cuda_time": 0.040847384833716124, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 484.96, "cuda_time_us": 139.903, "pct_cuda_time": 1.9208980438290373, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 160.551, "cuda_time_us": 84.607, "pct_cuda_time": 1.1616721642441077, "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": 84.607, "pct_cuda_time": 1.1616721642441077, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 91.857, "cuda_time_us": 9.056, "pct_cuda_time": 0.12434081245517083, "trace": "" }, "children": [ { "entry": { "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": 0.12434081245517083, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 164.186, "cuda_time_us": 46.24, "pct_cuda_time": 0.6348850671297592, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 43.84, "pct_cuda_time": 0.6019325549949966, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.4, "pct_cuda_time": 0.032952512134762586, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2622.395, "cuda_time_us": 208.829, "pct_cuda_time": 2.86726673191264, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 68.533, "cuda_time_us": 3.296, "pct_cuda_time": 0.045254783331740614, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045254783331740614, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1880.325, "cuda_time_us": 63.39, "pct_cuda_time": 0.8703582267594169, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 157.028, "cuda_time_us": 22.368, "pct_cuda_time": 0.3071174130959873, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.368, "pct_cuda_time": 0.3071174130959873, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 495.786, "cuda_time_us": 3.775, "pct_cuda_time": 0.05183155554530365, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05183155554530365, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 828.92, "cuda_time_us": 21.151, "pct_cuda_time": 0.2904077434009848, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03514934627708009, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.311, "pct_cuda_time": 0.23768372398536466, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.017574673138540046, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 216.09, "cuda_time_us": 16.096, "pct_cuda_time": 0.2210015147171411, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.096, "pct_cuda_time": 0.2210015147171411, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 85.833, "cuda_time_us": 3.072, "pct_cuda_time": 0.04217921553249611, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04217921553249611, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 506.865, "cuda_time_us": 139.071, "pct_cuda_time": 1.9094745062889866, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 171.57, "cuda_time_us": 83.839, "pct_cuda_time": 1.1511273603609835, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 83.839, "pct_cuda_time": 1.1511273603609835, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 109.113, "cuda_time_us": 8.832, "pct_cuda_time": 0.12126524465592634, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 8.832, "pct_cuda_time": 0.12126524465592634, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 143.876, "cuda_time_us": 46.4, "pct_cuda_time": 0.6370819012720766, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 43.936, "pct_cuda_time": 0.6032506554803871, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.464, "pct_cuda_time": 0.033831245791689585, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2687.821, "cuda_time_us": 211.101, "pct_cuda_time": 2.8984617767335488, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 75.798, "cuda_time_us": 3.136, "pct_cuda_time": 0.043057949189423114, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.043057949189423114, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1960.513, "cuda_time_us": 63.838, "pct_cuda_time": 0.8765093623579058, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 263.591, "cuda_time_us": 22.655, "pct_cuda_time": 0.31105798433876936, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.655, "pct_cuda_time": 0.31105798433876936, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 488.823, "cuda_time_us": 3.648, "pct_cuda_time": 0.05008781844483913, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05008781844483913, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 814.482, "cuda_time_us": 20.992, "pct_cuda_time": 0.2882246394720568, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 2.432, "pct_cuda_time": 0.03339187896322609, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.248, "pct_cuda_time": 0.23681872054182712, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 201.198, "cuda_time_us": 16.543, "pct_cuda_time": 0.22713892010224063, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.543, "pct_cuda_time": 0.22713892010224063, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 79.356, "cuda_time_us": 3.136, "pct_cuda_time": 0.043057949189423114, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.043057949189423114, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 486.238, "cuda_time_us": 140.99099999999999, "pct_cuda_time": 1.9358365159967965, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 171.673, "cuda_time_us": 85.247, "pct_cuda_time": 1.1704595008133776, "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": 85.247, "pct_cuda_time": 1.1704595008133776, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 98.318, "cuda_time_us": 9.216, "pct_cuda_time": 0.1265376465974883, "trace": "" }, "children": [ { "entry": { "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": 0.1265376465974883, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 164.467, "cuda_time_us": 46.528000000000006, "pct_cuda_time": 0.6388393685859308, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 43.968, "pct_cuda_time": 0.6036900223088506, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.56, "pct_cuda_time": 0.03514934627708009, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2517.951, "cuda_time_us": 210.39800000000002, "pct_cuda_time": 2.8888094367207415, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 78.028, "cuda_time_us": 3.072, "pct_cuda_time": 0.04217921553249611, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04217921553249611, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1804.482, "cuda_time_us": 63.906000000000006, "pct_cuda_time": 0.8774430168683909, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 181.637, "cuda_time_us": 21.92, "pct_cuda_time": 0.3009662774974983, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 21.92, "pct_cuda_time": 0.3009662774974983, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 477.131, "cuda_time_us": 3.713, "pct_cuda_time": 0.05098028231515562, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05098028231515562, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 793.888, "cuda_time_us": 21.313000000000002, "pct_cuda_time": 0.2926320379700813, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 2.497, "pct_cuda_time": 0.03428434283354257, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.504, "pct_cuda_time": 0.24033365516953514, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 189.701, "cuda_time_us": 16.96, "pct_cuda_time": 0.2328644190856556, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.96, "pct_cuda_time": 0.2328644190856556, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 79.982, "cuda_time_us": 2.912, "pct_cuda_time": 0.0399823813901786, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 2.912, "pct_cuda_time": 0.0399823813901786, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 465.194, "cuda_time_us": 140.508, "pct_cuda_time": 1.9292048229296759, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 158.437, "cuda_time_us": 84.158, "pct_cuda_time": 1.1555072984322292, "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": 84.158, "pct_cuda_time": 1.1555072984322292, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 93.391, "cuda_time_us": 9.248, "pct_cuda_time": 0.12697701342595183, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 9.248, "pct_cuda_time": 0.12697701342595183, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 155.318, "cuda_time_us": 47.102, "pct_cuda_time": 0.6467205110714946, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.479, "pct_cuda_time": 0.6107061613508771, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.623, "pct_cuda_time": 0.036014349720617615, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2710.142, "cuda_time_us": 212.029, "pct_cuda_time": 2.9112034147589902, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 67.498, "cuda_time_us": 3.168, "pct_cuda_time": 0.04349731601788662, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04349731601788662, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1908.431, "cuda_time_us": 64.511, "pct_cuda_time": 0.8857497959690288, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 156.814, "cuda_time_us": 22.912, "pct_cuda_time": 0.31458664917986684, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.912, "pct_cuda_time": 0.31458664917986684, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 505.86, "cuda_time_us": 3.68, "pct_cuda_time": 0.05052718527330264, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05052718527330264, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 812.096, "cuda_time_us": 21.439, "pct_cuda_time": 0.2943620448571563, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03427061262015309, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.664, "pct_cuda_time": 0.24253048931185267, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", "cpu_time_us": 0, "cuda_time_us": 1.279, "pct_cuda_time": 0.017560942925150563, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 199.798, "cuda_time_us": 16.48, "pct_cuda_time": 0.2262739166587031, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.48, "pct_cuda_time": 0.2262739166587031, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 90.959, "cuda_time_us": 3.008, "pct_cuda_time": 0.04130048187556911, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04130048187556911, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 542.099, "cuda_time_us": 141.342, "pct_cuda_time": 1.940655820896506, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 164.955, "cuda_time_us": 85.311, "pct_cuda_time": 1.1713382344703047, "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": 85.311, "pct_cuda_time": 1.1713382344703047, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 97.793, "cuda_time_us": 9.408, "pct_cuda_time": 0.12917384756826933, "trace": "" }, "children": [ { "entry": { "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": 0.12917384756826933, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 223.241, "cuda_time_us": 46.623000000000005, "pct_cuda_time": 0.6401437388579317, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.191, "pct_cuda_time": 0.6067518598947057, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.432, "pct_cuda_time": 0.03339187896322609, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2727.391, "cuda_time_us": 210.363, "pct_cuda_time": 2.888328879252109, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 81.651, "cuda_time_us": 3.072, "pct_cuda_time": 0.04217921553249611, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04217921553249611, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1991.456, "cuda_time_us": 63.772999999999996, "pct_cuda_time": 0.8756168984875893, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 166.188, "cuda_time_us": 21.983, "pct_cuda_time": 0.30183128094103584, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 21.983, "pct_cuda_time": 0.30183128094103584, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 503.172, "cuda_time_us": 3.936, "pct_cuda_time": 0.05404211990101064, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 3.936, "pct_cuda_time": 0.05404211990101064, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 937.749, "cuda_time_us": 21.471, "pct_cuda_time": 0.2948014116856198, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03514934627708009, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.631, "pct_cuda_time": 0.24207739226999964, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.017574673138540046, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 183.649, "cuda_time_us": 16.383, "pct_cuda_time": 0.2249420859599231, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.383, "pct_cuda_time": 0.2249420859599231, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 83.576, "cuda_time_us": 2.976, "pct_cuda_time": 0.04086111504710561, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 2.976, "pct_cuda_time": 0.04086111504710561, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 496.901, "cuda_time_us": 140.542, "pct_cuda_time": 1.9296716501849183, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 163.667, "cuda_time_us": 84.958, "pct_cuda_time": 1.1664914691438166, "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": 84.958, "pct_cuda_time": 1.1664914691438166, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 107.334, "cuda_time_us": 9.024, "pct_cuda_time": 0.12390144562670732, "trace": "" }, "children": [ { "entry": { "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": 0.12390144562670732, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 165.828, "cuda_time_us": 46.56, "pct_cuda_time": 0.6392787354143942, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.128, "pct_cuda_time": 0.605886856451168, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.432, "pct_cuda_time": 0.03339187896322609, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2547.8, "cuda_time_us": 212.66900000000004, "pct_cuda_time": 2.919990751328261, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 81.57, "cuda_time_us": 3.168, "pct_cuda_time": 0.04349731601788662, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04349731601788662, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1822.332, "cuda_time_us": 64.543, "pct_cuda_time": 0.8861891627974924, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 161.928, "cuda_time_us": 22.911, "pct_cuda_time": 0.3145729189664774, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.911, "pct_cuda_time": 0.3145729189664774, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 484.511, "cuda_time_us": 3.68, "pct_cuda_time": 0.05052718527330264, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05052718527330264, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 824.624, "cuda_time_us": 21.791, "pct_cuda_time": 0.29919507997025485, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.0360280799340071, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.855, "pct_cuda_time": 0.24515296006924417, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 195.127, "cuda_time_us": 16.161, "pct_cuda_time": 0.2218939785874576, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.161, "pct_cuda_time": 0.2218939785874576, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 79.84, "cuda_time_us": 3.04, "pct_cuda_time": 0.04173984870403261, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04173984870403261, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 491.812, "cuda_time_us": 141.918, "pct_cuda_time": 1.9485644238088486, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 163.027, "cuda_time_us": 84.863, "pct_cuda_time": 1.1651870988718156, "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": 84.863, "pct_cuda_time": 1.1651870988718156, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 99.471, "cuda_time_us": 9.024, "pct_cuda_time": 0.12390144562670732, "trace": "" }, "children": [ { "entry": { "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": 0.12390144562670732, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 163.2, "cuda_time_us": 48.031, "pct_cuda_time": 0.6594758793103258, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 45.535, "pct_cuda_time": 0.6252052666901726, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.496, "pct_cuda_time": 0.03427061262015309, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2651.469, "cuda_time_us": 210.33299999999997, "pct_cuda_time": 2.8879169728504244, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 73.23, "cuda_time_us": 3.104, "pct_cuda_time": 0.04261858236095961, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04261858236095961, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1919.231, "cuda_time_us": 63.870999999999995, "pct_cuda_time": 0.8769624593997587, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 174.167, "cuda_time_us": 21.984, "pct_cuda_time": 0.3018450111544253, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 21.984, "pct_cuda_time": 0.3018450111544253, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 527.579, "cuda_time_us": 3.744, "pct_cuda_time": 0.05140591893022964, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05140591893022964, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 848.561, "cuda_time_us": 21.727, "pct_cuda_time": 0.29831634631332776, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03470997944861659, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.728, "pct_cuda_time": 0.24340922296877968, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", "cpu_time_us": 0, "cuda_time_us": 1.471, "pct_cuda_time": 0.02019714389593157, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 179.702, "cuda_time_us": 16.416, "pct_cuda_time": 0.22539518300177608, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.416, "pct_cuda_time": 0.22539518300177608, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 79.564, "cuda_time_us": 2.848, "pct_cuda_time": 0.039103647733251604, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 2.848, "pct_cuda_time": 0.039103647733251604, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 500.928, "cuda_time_us": 140.51, "pct_cuda_time": 1.9292322833564544, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 162.942, "cuda_time_us": 84.671, "pct_cuda_time": 1.1625508979010346, "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": 84.671, "pct_cuda_time": 1.1625508979010346, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 113.175, "cuda_time_us": 9.184, "pct_cuda_time": 0.12609827976902482, "trace": "" }, "children": [ { "entry": { "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": 0.12609827976902482, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 164.04, "cuda_time_us": 46.654999999999994, "pct_cuda_time": 0.6405831056863951, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.224, "pct_cuda_time": 0.6072049569365585, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.431, "pct_cuda_time": 0.033378148749836606, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2503.445, "cuda_time_us": 210.079, "pct_cuda_time": 2.884429498649496, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 70.476, "cuda_time_us": 3.264, "pct_cuda_time": 0.04481541650327712, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04481541650327712, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1796.64, "cuda_time_us": 64.19300000000001, "pct_cuda_time": 0.8813835881111729, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 152.485, "cuda_time_us": 22.624, "pct_cuda_time": 0.3106323477236953, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.624, "pct_cuda_time": 0.3106323477236953, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 481.428, "cuda_time_us": 3.648, "pct_cuda_time": 0.05008781844483913, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05008781844483913, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 802.723, "cuda_time_us": 21.345000000000002, "pct_cuda_time": 0.29307140479854477, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 2.657, "pct_cuda_time": 0.036481176975860084, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.28, "pct_cuda_time": 0.23725808737029064, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01933214045239405, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 187.279, "cuda_time_us": 16.576, "pct_cuda_time": 0.2275920171440936, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.576, "pct_cuda_time": 0.2275920171440936, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 75.113, "cuda_time_us": 2.976, "pct_cuda_time": 0.04086111504710561, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 2.976, "pct_cuda_time": 0.04086111504710561, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 484.968, "cuda_time_us": 139.64600000000002, "pct_cuda_time": 1.9173693789879405, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 154.821, "cuda_time_us": 84.031, "pct_cuda_time": 1.1537635613317645, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 84.031, "pct_cuda_time": 1.1537635613317645, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 95.449, "cuda_time_us": 9.184, "pct_cuda_time": 0.12609827976902482, "trace": "" }, "children": [ { "entry": { "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": 0.12609827976902482, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 177.014, "cuda_time_us": 46.431000000000004, "pct_cuda_time": 0.6375075378871508, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 43.999, "pct_cuda_time": 0.6041156589239247, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.432, "pct_cuda_time": 0.03339187896322609, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2751.569, "cuda_time_us": 210.591, "pct_cuda_time": 2.8914593679049116, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 69.623, "cuda_time_us": 3.328, "pct_cuda_time": 0.04569415016020412, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 3.328, "pct_cuda_time": 0.04569415016020412, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1993.717, "cuda_time_us": 63.167, "pct_cuda_time": 0.8672963891735618, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 174.184, "cuda_time_us": 21.727, "pct_cuda_time": 0.29831634631332776, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 21.727, "pct_cuda_time": 0.29831634631332776, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 511.292, "cuda_time_us": 3.744, "pct_cuda_time": 0.05140591893022964, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05140591893022964, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 908.263, "cuda_time_us": 21.376, "pct_cuda_time": 0.29349704141361876, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03470997944861659, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.568, "pct_cuda_time": 0.24121238882646215, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.017574673138540046, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 203.351, "cuda_time_us": 16.32, "pct_cuda_time": 0.2240770825163856, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.32, "pct_cuda_time": 0.2240770825163856, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 93.11, "cuda_time_us": 3.072, "pct_cuda_time": 0.04217921553249611, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04217921553249611, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 513.433, "cuda_time_us": 141.024, "pct_cuda_time": 1.9362896130386498, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 172.675, "cuda_time_us": 84.863, "pct_cuda_time": 1.1651870988718156, "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": 84.863, "pct_cuda_time": 1.1651870988718156, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 107.552, "cuda_time_us": 8.993, "pct_cuda_time": 0.12347580901163331, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 8.993, "pct_cuda_time": 0.12347580901163331, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 172.841, "cuda_time_us": 47.168, "pct_cuda_time": 0.6476267051552007, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.704, "pct_cuda_time": 0.6137954593635112, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.464, "pct_cuda_time": 0.033831245791689585, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2639.67, "cuda_time_us": 210.30200000000002, "pct_cuda_time": 2.887491336235351, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 82.254, "cuda_time_us": 3.104, "pct_cuda_time": 0.04261858236095961, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04261858236095961, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1846.565, "cuda_time_us": 64.19200000000001, "pct_cuda_time": 0.8813698578977834, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 164.164, "cuda_time_us": 22.848, "pct_cuda_time": 0.3137079155229398, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.848, "pct_cuda_time": 0.3137079155229398, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 505.017, "cuda_time_us": 3.744, "pct_cuda_time": 0.05140591893022964, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05140591893022964, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 812.073, "cuda_time_us": 21.152, "pct_cuda_time": 0.29042147361437426, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03427061262015309, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.344, "pct_cuda_time": 0.23813682102721764, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 175.451, "cuda_time_us": 16.448, "pct_cuda_time": 0.2258345498302396, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.448, "pct_cuda_time": 0.2258345498302396, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 79.353, "cuda_time_us": 2.976, "pct_cuda_time": 0.04086111504710561, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 2.976, "pct_cuda_time": 0.04086111504710561, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 534.433, "cuda_time_us": 140.03, "pct_cuda_time": 1.9226417809295022, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 166.613, "cuda_time_us": 84.382, "pct_cuda_time": 1.1585828662314737, "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": 84.382, "pct_cuda_time": 1.1585828662314737, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 120.301, "cuda_time_us": 9.12, "pct_cuda_time": 0.12521954611209782, "trace": "" }, "children": [ { "entry": { "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": 0.12521954611209782, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 188.871, "cuda_time_us": 46.528, "pct_cuda_time": 0.6388393685859307, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.096, "pct_cuda_time": 0.6054474896227046, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.432, "pct_cuda_time": 0.03339187896322609, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2630.273, "cuda_time_us": 209.791, "pct_cuda_time": 2.880475197193324, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 77.241, "cuda_time_us": 3.104, "pct_cuda_time": 0.04261858236095961, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04261858236095961, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1864.663, "cuda_time_us": 63.392, "pct_cuda_time": 0.8703856871861959, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 163.463, "cuda_time_us": 21.984, "pct_cuda_time": 0.3018450111544253, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 21.984, "pct_cuda_time": 0.3018450111544253, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 481.381, "cuda_time_us": 3.84, "pct_cuda_time": 0.05272401941562014, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05272401941562014, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 840.556, "cuda_time_us": 21.312, "pct_cuda_time": 0.2926183077566918, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03470997944861659, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.472, "pct_cuda_time": 0.23989428834107163, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 202.838, "cuda_time_us": 16.256, "pct_cuda_time": 0.2231983488594586, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.256, "pct_cuda_time": 0.2231983488594586, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 86.906, "cuda_time_us": 3.008, "pct_cuda_time": 0.04130048187556911, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04130048187556911, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 522.854, "cuda_time_us": 140.28699999999998, "pct_cuda_time": 1.9261704457705993, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 158.978, "cuda_time_us": 84.19, "pct_cuda_time": 1.1559466652606927, "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": 84.19, "pct_cuda_time": 1.1559466652606927, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 120.697, "cuda_time_us": 9.056, "pct_cuda_time": 0.12434081245517083, "trace": "" }, "children": [ { "entry": { "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": 0.12434081245517083, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 176.867, "cuda_time_us": 47.041, "pct_cuda_time": 0.6458829680547362, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.576, "pct_cuda_time": 0.6120379920496571, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.465, "pct_cuda_time": 0.03384497600507907, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2892.723, "cuda_time_us": 210.077, "pct_cuda_time": 2.8844020382227167, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 77.076, "cuda_time_us": 3.072, "pct_cuda_time": 0.04217921553249611, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04217921553249611, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1937.397, "cuda_time_us": 64.768, "pct_cuda_time": 0.8892784608101263, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 147.54, "cuda_time_us": 23.392, "pct_cuda_time": 0.3211771516068193, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 23.392, "pct_cuda_time": 0.3211771516068193, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 494.532, "cuda_time_us": 3.648, "pct_cuda_time": 0.05008781844483913, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05008781844483913, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 916.256, "cuda_time_us": 21.056, "pct_cuda_time": 0.28910337312898376, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 2.464, "pct_cuda_time": 0.033831245791689585, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.28, "pct_cuda_time": 0.23725808737029064, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 180.551, "cuda_time_us": 16.672, "pct_cuda_time": 0.2289101176294841, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.672, "pct_cuda_time": 0.2289101176294841, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 101.149, "cuda_time_us": 2.976, "pct_cuda_time": 0.04086111504710561, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 2.976, "pct_cuda_time": 0.04086111504710561, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 692.488, "cuda_time_us": 139.261, "pct_cuda_time": 1.9120832468329887, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 169.584, "cuda_time_us": 84.223, "pct_cuda_time": 1.1563997623025455, "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": 84.223, "pct_cuda_time": 1.1563997623025455, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 99.777, "cuda_time_us": 8.991, "pct_cuda_time": 0.12344834858485434, "trace": "" }, "children": [ { "entry": { "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": 0.12344834858485434, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 363.228, "cuda_time_us": 46.047000000000004, "pct_cuda_time": 0.6322351359455888, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 43.615, "pct_cuda_time": 0.5988432569823626, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.432, "pct_cuda_time": 0.03339187896322609, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2724.532, "cuda_time_us": 208.12800000000001, "pct_cuda_time": 2.8576418523266116, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 77.714, "cuda_time_us": 3.232, "pct_cuda_time": 0.04437604967481362, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04437604967481362, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1931.091, "cuda_time_us": 63.104000000000006, "pct_cuda_time": 0.8664313857300244, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 171.535, "cuda_time_us": 21.92, "pct_cuda_time": 0.3009662774974983, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 21.92, "pct_cuda_time": 0.3009662774974983, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 505.984, "cuda_time_us": 3.776, "pct_cuda_time": 0.051845285758693134, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.051845285758693134, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 840.558, "cuda_time_us": 21.216, "pct_cuda_time": 0.2913002072713013, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03470997944861659, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.408, "pct_cuda_time": 0.23901555468414465, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.017574673138540046, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 212.926, "cuda_time_us": 16.192, "pct_cuda_time": 0.22231961520253157, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.192, "pct_cuda_time": 0.22231961520253157, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 111.706, "cuda_time_us": 2.976, "pct_cuda_time": 0.04086111504710561, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 2.976, "pct_cuda_time": 0.04086111504710561, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 525.934, "cuda_time_us": 138.816, "pct_cuda_time": 1.905973301874668, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 176.965, "cuda_time_us": 84.191, "pct_cuda_time": 1.155960395474082, "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": 84.191, "pct_cuda_time": 1.155960395474082, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 129.713, "cuda_time_us": 8.928, "pct_cuda_time": 0.12258334514131683, "trace": "" }, "children": [ { "entry": { "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": 0.12258334514131683, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 156.043, "cuda_time_us": 45.697, "pct_cuda_time": 0.6274295612592692, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 43.232, "pct_cuda_time": 0.59358458525419, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.465, "pct_cuda_time": 0.03384497600507907, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2639.772, "cuda_time_us": 211.26000000000002, "pct_cuda_time": 2.900644880662477, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 77.746, "cuda_time_us": 3.072, "pct_cuda_time": 0.04217921553249611, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04217921553249611, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1873.419, "cuda_time_us": 64.095, "pct_cuda_time": 0.8800380271990033, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 164.026, "cuda_time_us": 22.848, "pct_cuda_time": 0.3137079155229398, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.848, "pct_cuda_time": 0.3137079155229398, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 491.507, "cuda_time_us": 3.648, "pct_cuda_time": 0.05008781844483913, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05008781844483913, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 837.954, "cuda_time_us": 21.28, "pct_cuda_time": 0.29217894092822827, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03427061262015309, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.504, "pct_cuda_time": 0.24033365516953514, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.017574673138540046, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 191.99, "cuda_time_us": 16.319, "pct_cuda_time": 0.2240633523029961, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.319, "pct_cuda_time": 0.2240633523029961, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 105.939, "cuda_time_us": 3.168, "pct_cuda_time": 0.04349731601788662, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04349731601788662, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 498.988, "cuda_time_us": 140.925, "pct_cuda_time": 1.934930321913091, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 142.976, "cuda_time_us": 84.575, "pct_cuda_time": 1.1612327974156442, "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": 84.575, "pct_cuda_time": 1.1612327974156442, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 123.996, "cuda_time_us": 9.248, "pct_cuda_time": 0.12697701342595183, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 9.248, "pct_cuda_time": 0.12697701342595183, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 169.507, "cuda_time_us": 47.102000000000004, "pct_cuda_time": 0.6467205110714948, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.639, "pct_cuda_time": 0.6129029954931947, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.463, "pct_cuda_time": 0.0338175155783001, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2744.059, "cuda_time_us": 211.74099999999999, "pct_cuda_time": 2.9072491133028184, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 78.754, "cuda_time_us": 3.167, "pct_cuda_time": 0.04348358580449713, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 3.167, "pct_cuda_time": 0.04348358580449713, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1985.917, "cuda_time_us": 63.775999999999996, "pct_cuda_time": 0.8756580891277578, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 174.866, "cuda_time_us": 21.76, "pct_cuda_time": 0.2987694433551808, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 21.76, "pct_cuda_time": 0.2987694433551808, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 547.265, "cuda_time_us": 3.776, "pct_cuda_time": 0.051845285758693134, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.051845285758693134, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 867.956, "cuda_time_us": 21.887999999999998, "pct_cuda_time": 0.30052691066903475, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03470997944861659, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 18.08, "pct_cuda_time": 0.24824225808187814, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.017574673138540046, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 226.358, "cuda_time_us": 16.352, "pct_cuda_time": 0.22451644934484907, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.352, "pct_cuda_time": 0.22451644934484907, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 86.269, "cuda_time_us": 2.976, "pct_cuda_time": 0.04086111504710561, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 2.976, "pct_cuda_time": 0.04086111504710561, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 508.72, "cuda_time_us": 141.822, "pct_cuda_time": 1.9472463233234583, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 167.112, "cuda_time_us": 85.503, "pct_cuda_time": 1.1739744354410857, "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": 85.503, "pct_cuda_time": 1.1739744354410857, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 114.771, "cuda_time_us": 9.28, "pct_cuda_time": 0.12741638025441535, "trace": "" }, "children": [ { "entry": { "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": 0.12741638025441535, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 166.175, "cuda_time_us": 47.039, "pct_cuda_time": 0.6458555076279573, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.575, "pct_cuda_time": 0.6120242618362677, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.464, "pct_cuda_time": 0.033831245791689585, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2676.907, "cuda_time_us": 210.17399999999998, "pct_cuda_time": 2.885733868921496, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 83.333, "cuda_time_us": 3.104, "pct_cuda_time": 0.04261858236095961, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04261858236095961, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1921.754, "cuda_time_us": 64.128, "pct_cuda_time": 0.8804911242408564, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 178.663, "cuda_time_us": 22.592, "pct_cuda_time": 0.31019298089523184, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.592, "pct_cuda_time": 0.31019298089523184, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 499.898, "cuda_time_us": 3.776, "pct_cuda_time": 0.051845285758693134, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.051845285758693134, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 852.956, "cuda_time_us": 21.28, "pct_cuda_time": 0.29217894092822827, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03470997944861659, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.44, "pct_cuda_time": 0.23945492151260817, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 197.692, "cuda_time_us": 16.48, "pct_cuda_time": 0.2262739166587031, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.48, "pct_cuda_time": 0.2262739166587031, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 85.616, "cuda_time_us": 3.008, "pct_cuda_time": 0.04130048187556911, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04130048187556911, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 502.857, "cuda_time_us": 139.934, "pct_cuda_time": 1.9213236804441116, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 168.143, "cuda_time_us": 83.999, "pct_cuda_time": 1.153324194503301, "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": 83.999, "pct_cuda_time": 1.153324194503301, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 104.175, "cuda_time_us": 9.12, "pct_cuda_time": 0.12521954611209782, "trace": "" }, "children": [ { "entry": { "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": 0.12521954611209782, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 170.862, "cuda_time_us": 46.815, "pct_cuda_time": 0.6427799398287127, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.223, "pct_cuda_time": 0.6071912267231692, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.592, "pct_cuda_time": 0.035588713105543596, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2556.175, "cuda_time_us": 210.847, "pct_cuda_time": 2.89497430253262, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 80.874, "cuda_time_us": 3.105, "pct_cuda_time": 0.042632312574349095, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.042632312574349095, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1822.197, "cuda_time_us": 63.425, "pct_cuda_time": 0.8708387842280488, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 164.998, "cuda_time_us": 22.08, "pct_cuda_time": 0.3031631116398158, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.08, "pct_cuda_time": 0.3031631116398158, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 462.923, "cuda_time_us": 3.68, "pct_cuda_time": 0.05052718527330264, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05052718527330264, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 803.938, "cuda_time_us": 21.313000000000002, "pct_cuda_time": 0.2926320379700813, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 2.529, "pct_cuda_time": 0.03472370966200608, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.504, "pct_cuda_time": 0.24033365516953514, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.017574673138540046, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 201.367, "cuda_time_us": 16.352, "pct_cuda_time": 0.22451644934484907, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.352, "pct_cuda_time": 0.22451644934484907, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 86.917, "cuda_time_us": 3.232, "pct_cuda_time": 0.04437604967481362, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04437604967481362, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 487.997, "cuda_time_us": 141.085, "pct_cuda_time": 1.9371271560554082, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 167.672, "cuda_time_us": 84.254, "pct_cuda_time": 1.1568253989176196, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 84.254, "pct_cuda_time": 1.1568253989176196, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 97.39, "cuda_time_us": 9.312, "pct_cuda_time": 0.12785574708287883, "trace": "" }, "children": [ { "entry": { "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": 0.12785574708287883, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 162.031, "cuda_time_us": 47.519000000000005, "pct_cuda_time": 0.6524460100549098, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 45.087, "pct_cuda_time": 0.6190541310916837, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.432, "pct_cuda_time": 0.03339187896322609, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2669.749, "cuda_time_us": 209.91500000000002, "pct_cuda_time": 2.8821777436536204, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 73.283, "cuda_time_us": 3.135, "pct_cuda_time": 0.043044218976033624, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 3.135, "pct_cuda_time": 0.043044218976033624, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1921.751, "cuda_time_us": 63.902, "pct_cuda_time": 0.8773880960148328, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 160.86, "cuda_time_us": 22.751, "pct_cuda_time": 0.31237608482415985, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.751, "pct_cuda_time": 0.31237608482415985, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 534.116, "cuda_time_us": 3.648, "pct_cuda_time": 0.05008781844483913, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05008781844483913, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 843.409, "cuda_time_us": 21.152, "pct_cuda_time": 0.29042147361437426, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03470997944861659, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.312, "pct_cuda_time": 0.23769745419875413, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 174.279, "cuda_time_us": 16.351, "pct_cuda_time": 0.22450271913145958, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.351, "pct_cuda_time": 0.22450271913145958, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 82.676, "cuda_time_us": 3.008, "pct_cuda_time": 0.04130048187556911, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04130048187556911, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 495.947, "cuda_time_us": 139.87, "pct_cuda_time": 1.9204449467871847, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 159.372, "cuda_time_us": 83.807, "pct_cuda_time": 1.15068799353252, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 83.807, "pct_cuda_time": 1.15068799353252, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 103.086, "cuda_time_us": 8.991, "pct_cuda_time": 0.12344834858485434, "trace": "" }, "children": [ { "entry": { "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": 0.12344834858485434, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 174.504, "cuda_time_us": 47.071999999999996, "pct_cuda_time": 0.6463086046698102, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.608, "pct_cuda_time": 0.6124773588781206, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.464, "pct_cuda_time": 0.033831245791689585, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2614.439, "cuda_time_us": 209.72500000000002, "pct_cuda_time": 2.8795690031096184, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 78.449, "cuda_time_us": 3.296, "pct_cuda_time": 0.045254783331740614, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045254783331740614, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1882.953, "cuda_time_us": 63.455, "pct_cuda_time": 0.8712506906297333, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 161.475, "cuda_time_us": 21.824, "pct_cuda_time": 0.29964817701210783, "trace": "" }, "children": [ { "entry": { "name": "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": 0.29964817701210783, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 519.653, "cuda_time_us": 3.872, "pct_cuda_time": 0.05316338624408364, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05316338624408364, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 821.429, "cuda_time_us": 21.247, "pct_cuda_time": 0.2917258438863753, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03470997944861659, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.407, "pct_cuda_time": 0.23900182447075513, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 184.833, "cuda_time_us": 16.512, "pct_cuda_time": 0.2267132834871666, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.512, "pct_cuda_time": 0.2267132834871666, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 84.862, "cuda_time_us": 2.944, "pct_cuda_time": 0.040421748218642105, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 2.944, "pct_cuda_time": 0.040421748218642105, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 486.611, "cuda_time_us": 140.03, "pct_cuda_time": 1.9226417809295022, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 162.747, "cuda_time_us": 84.863, "pct_cuda_time": 1.1651870988718156, "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": 84.863, "pct_cuda_time": 1.1651870988718156, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 99.941, "cuda_time_us": 9.184, "pct_cuda_time": 0.12609827976902482, "trace": "" }, "children": [ { "entry": { "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": 0.12609827976902482, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 163.804, "cuda_time_us": 45.983, "pct_cuda_time": 0.6313564022886616, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 43.519, "pct_cuda_time": 0.597525156496972, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.464, "pct_cuda_time": 0.033831245791689585, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2865.599, "cuda_time_us": 211.169, "pct_cuda_time": 2.8993954312440335, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 77.162, "cuda_time_us": 3.104, "pct_cuda_time": 0.04261858236095961, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04261858236095961, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 2128.305, "cuda_time_us": 64.321, "pct_cuda_time": 0.8831410554250269, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 168.739, "cuda_time_us": 22.752, "pct_cuda_time": 0.3123898150375493, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.752, "pct_cuda_time": 0.3123898150375493, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 566.07, "cuda_time_us": 3.681, "pct_cuda_time": 0.05054091548669212, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", "cpu_time_us": 0, "cuda_time_us": 3.681, "pct_cuda_time": 0.05054091548669212, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 1028.958, "cuda_time_us": 21.504, "pct_cuda_time": 0.2952545087274728, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.0360280799340071, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.568, "pct_cuda_time": 0.24121238882646215, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 199.042, "cuda_time_us": 16.384, "pct_cuda_time": 0.22495581617331262, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.384, "pct_cuda_time": 0.22495581617331262, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 88.338, "cuda_time_us": 3.009, "pct_cuda_time": 0.04131421208895859, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 3.009, "pct_cuda_time": 0.04131421208895859, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 491.607, "cuda_time_us": 140.735, "pct_cuda_time": 1.9323215813690886, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 157.342, "cuda_time_us": 84.927, "pct_cuda_time": 1.1660658325287425, "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": 84.927, "pct_cuda_time": 1.1660658325287425, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 123.825, "cuda_time_us": 9.12, "pct_cuda_time": 0.12521954611209782, "trace": "" }, "children": [ { "entry": { "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": 0.12521954611209782, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 151.586, "cuda_time_us": 46.687999999999995, "pct_cuda_time": 0.6410362027282481, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.224, "pct_cuda_time": 0.6072049569365585, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.464, "pct_cuda_time": 0.033831245791689585, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2518.649, "cuda_time_us": 210.812, "pct_cuda_time": 2.894493745063988, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 95.097, "cuda_time_us": 3.104, "pct_cuda_time": 0.04261858236095961, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04261858236095961, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1778.113, "cuda_time_us": 64.031, "pct_cuda_time": 0.8791592935420764, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 144.283, "cuda_time_us": 21.728, "pct_cuda_time": 0.2983300765267173, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 21.728, "pct_cuda_time": 0.2983300765267173, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 502.834, "cuda_time_us": 3.712, "pct_cuda_time": 0.050966552101766135, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.050966552101766135, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 770.863, "cuda_time_us": 21.983999999999998, "pct_cuda_time": 0.3018450111544253, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03470997944861659, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 18.144, "pct_cuda_time": 0.24912099173880511, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 188.894, "cuda_time_us": 16.607, "pct_cuda_time": 0.2280176537591676, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.607, "pct_cuda_time": 0.2280176537591676, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 84.278, "cuda_time_us": 3.04, "pct_cuda_time": 0.04173984870403261, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04173984870403261, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 467.681, "cuda_time_us": 140.637, "pct_cuda_time": 1.930976020456919, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 154.107, "cuda_time_us": 84.191, "pct_cuda_time": 1.155960395474082, "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": 84.191, "pct_cuda_time": 1.155960395474082, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 101.425, "cuda_time_us": 8.991, "pct_cuda_time": 0.12344834858485434, "trace": "" }, "children": [ { "entry": { "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": 0.12344834858485434, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 151.552, "cuda_time_us": 47.455, "pct_cuda_time": 0.6515672763979827, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.799, "pct_cuda_time": 0.6150998296355121, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.656, "pct_cuda_time": 0.0364674467624706, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2623.494, "cuda_time_us": 210.971, "pct_cuda_time": 2.8966768489929158, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 79.507, "cuda_time_us": 3.071, "pct_cuda_time": 0.042165485319106626, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.042165485319106626, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1918.15, "cuda_time_us": 64.158, "pct_cuda_time": 0.8809030306425408, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 146.956, "cuda_time_us": 22.624, "pct_cuda_time": 0.3106323477236953, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.624, "pct_cuda_time": 0.3106323477236953, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 522.271, "cuda_time_us": 3.68, "pct_cuda_time": 0.05052718527330264, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05052718527330264, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 802.382, "cuda_time_us": 21.247, "pct_cuda_time": 0.2917258438863753, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 2.592, "pct_cuda_time": 0.035588713105543596, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.343, "pct_cuda_time": 0.23812309081382815, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 248.114, "cuda_time_us": 16.607, "pct_cuda_time": 0.2280176537591676, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.607, "pct_cuda_time": 0.2280176537591676, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 78.522, "cuda_time_us": 3.04, "pct_cuda_time": 0.04173984870403261, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04173984870403261, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 466.052, "cuda_time_us": 140.702, "pct_cuda_time": 1.9318684843272358, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 155.536, "cuda_time_us": 84.991, "pct_cuda_time": 1.1669445661856697, "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": 84.991, "pct_cuda_time": 1.1669445661856697, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 96.654, "cuda_time_us": 9.248, "pct_cuda_time": 0.12697701342595183, "trace": "" }, "children": [ { "entry": { "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", "cpu_time_us": 0, "cuda_time_us": 9.248, "pct_cuda_time": 0.12697701342595183, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 147.796, "cuda_time_us": 46.463, "pct_cuda_time": 0.6379469047156142, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.063, "pct_cuda_time": 0.6049943925808516, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.4, "pct_cuda_time": 0.032952512134762586, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2527.799, "cuda_time_us": 209.821, "pct_cuda_time": 2.8808871035950085, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 69.613, "cuda_time_us": 3.167, "pct_cuda_time": 0.04348358580449713, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", "cpu_time_us": 0, "cuda_time_us": 3.167, "pct_cuda_time": 0.04348358580449713, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1847.564, "cuda_time_us": 63.169, "pct_cuda_time": 0.8673238496003407, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 144.972, "cuda_time_us": 21.856, "pct_cuda_time": 0.30008754384057135, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 21.856, "pct_cuda_time": 0.30008754384057135, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 532.785, "cuda_time_us": 3.712, "pct_cuda_time": 0.050966552101766135, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.050966552101766135, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 813.645, "cuda_time_us": 21.441, "pct_cuda_time": 0.29438950528393526, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", "cpu_time_us": 0, "cuda_time_us": 2.656, "pct_cuda_time": 0.0364674467624706, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.408, "pct_cuda_time": 0.23901555468414465, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", "cpu_time_us": 0, "cuda_time_us": 1.377, "pct_cuda_time": 0.018906503837320034, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 177.265, "cuda_time_us": 16.16, "pct_cuda_time": 0.22188024837406808, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.16, "pct_cuda_time": 0.22188024837406808, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 84.023, "cuda_time_us": 3.04, "pct_cuda_time": 0.04173984870403261, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04173984870403261, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 450.699, "cuda_time_us": 140.445, "pct_cuda_time": 1.928339819486138, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 148.279, "cuda_time_us": 84.606, "pct_cuda_time": 1.1616584340307181, "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": 84.606, "pct_cuda_time": 1.1616584340307181, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 100.019, "cuda_time_us": 9.056, "pct_cuda_time": 0.12434081245517083, "trace": "" }, "children": [ { "entry": { "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": 0.12434081245517083, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 140.617, "cuda_time_us": 46.783, "pct_cuda_time": 0.6423405730002493, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 44.128, "pct_cuda_time": 0.605886856451168, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.655, "pct_cuda_time": 0.03645371654908111, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "LlamaDecoderLayer", "cpu_time_us": 2542.817, "cuda_time_us": 209.948, "pct_cuda_time": 2.882630840695473, "trace": "" }, "children": [ { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 75.354, "cuda_time_us": 3.136, "pct_cuda_time": 0.043057949189423114, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.043057949189423114, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaAttention", "cpu_time_us": 1850.294, "cuda_time_us": 64.191, "pct_cuda_time": 0.881356127684394, "trace": "" }, "children": [ { "entry": { "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", "cpu_time_us": 167.788, "cuda_time_us": 22.719, "pct_cuda_time": 0.31193671799569633, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 22.719, "pct_cuda_time": 0.31193671799569633, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[20, 4096], bfloat16[6144, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Llama3RotaryEmbedding", "cpu_time_us": 536.381, "cuda_time_us": 3.648, "pct_cuda_time": 0.05008781844483913, "trace": "" }, "children": [ { "entry": { "name": "void vllm::rotary_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": 0.05008781844483913, "trace": "_C::rotary_embedding(int64[20], bfloat16[20, 4096], bfloat16[20, 1024], 128, bfloat16[131072, 128], True)" }, "children": [] } ] }, { "entry": { "name": "Attention", "cpu_time_us": 795.382, "cuda_time_us": 21.152, "pct_cuda_time": 0.29042147361437426, "trace": "" }, "children": [ { "entry": { "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03427061262015309, "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[20], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", "cpu_time_us": 0, "cuda_time_us": 17.344, "pct_cuda_time": 0.23813682102721764, "trace": "_vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] }, { "entry": { "name": "void at::native::vectorized_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": 0.01801403996700355, "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[20, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[20, 1, 32, 128], None, None, None, None, int32[20], None, None, int32[20, 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[20, 32, 128], bfloat16[20, 8, 128], bfloat16[20, 8, 128], bfloat16[20, 32, 128], None)" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", "cpu_time_us": 174.515, "cuda_time_us": 16.672, "pct_cuda_time": 0.2289101176294841, "trace": "" }, "children": [ { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 16.672, "pct_cuda_time": 0.2289101176294841, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[20, 4096], bfloat16[4096, 4096], None)" }, "children": [] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 81.165, "cuda_time_us": 3.232, "pct_cuda_time": 0.04437604967481362, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04437604967481362, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] }, { "entry": { "name": "LlamaMLP", "cpu_time_us": 457.383, "cuda_time_us": 139.389, "pct_cuda_time": 1.9138407141468428, "trace": "" }, "children": [ { "entry": { "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", "cpu_time_us": 148.167, "cuda_time_us": 84.447, "pct_cuda_time": 1.1594753301017902, "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": 84.447, "pct_cuda_time": 1.1594753301017902, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[20, 4096], bfloat16[28672, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "SiluAndMul", "cpu_time_us": 97.188, "cuda_time_us": 8.992, "pct_cuda_time": 0.12346207879824385, "trace": "" }, "children": [ { "entry": { "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": 0.12346207879824385, "trace": "_C::silu_and_mul(bfloat16[20, 14336], bfloat16[20, 28672])" }, "children": [] } ] }, { "entry": { "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", "cpu_time_us": 147.471, "cuda_time_us": 45.95, "pct_cuda_time": 0.6309033052468087, "trace": "" }, "children": [ { "entry": { "name": "void cutlass::Kernel2(cutlass_80_tensorop_s16816gemm_bf16_128x64_64x6_tn_align8::Params)", "cpu_time_us": 0, "cuda_time_us": 43.551, "pct_cuda_time": 0.5979645233254356, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] }, { "entry": { "name": "void cublasLt::splitKreduce_kernel<32, 16, int, float, __nv_bfloat16, float, __nv_bfloat16, true, false, false>(cublasLt::cublasSplitKParams, float const*, __nv_bfloat16 const*, __nv_bfloat16*, float const*, float const*, __nv_bfloat16 const*, float const*, __nv_bfloat16*, void*, long, float*, int*)", "cpu_time_us": 0, "cuda_time_us": 2.399, "pct_cuda_time": 0.0329387819213731, "trace": "mm(bfloat16[20, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[20, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[20, 14336], bfloat16[4096, 14336], None)" }, "children": [] } ] } ] } ] }, { "entry": { "name": "RMSNorm(weight=bfloat16[4096])", "cpu_time_us": 74.784, "cuda_time_us": 3.104, "pct_cuda_time": 0.04261858236095961, "trace": "" }, "children": [ { "entry": { "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04261858236095961, "trace": "_C::fused_add_rms_norm(bfloat16[20, 4096], bfloat16[20, 4096], bfloat16[4096], 1e-05)" }, "children": [] } ] } ] }, { "entry": { "name": "LogitsProcessor", "cpu_time_us": 636.408, "cuda_time_us": 389.915, "pct_cuda_time": 5.3536161537608145, "trace": "" }, "children": [ { "entry": { "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", "cpu_time_us": 0, "cuda_time_us": 3.232, "pct_cuda_time": 0.04437604967481362, "trace": "index_select(bfloat16[20, 4096], 0, int64[20])" }, "children": [] }, { "entry": { "name": "Memset (Device)", "cpu_time_us": 0, "cuda_time_us": 0.736, "pct_cuda_time": 0.010105437054660526, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[20, 4096], bfloat16[128256, 4096], None)" }, "children": [] }, { "entry": { "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x256x64_warpgroupsize2x1x1_execute_segment_k_off_kernel__5x_cublas", "cpu_time_us": 0, "cuda_time_us": 385.947, "pct_cuda_time": 5.29913466703134, "trace": "mm(bfloat16[20, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[20, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[20, 4096], bfloat16[128256, 4096], None)" }, "children": [] } ] }, { "entry": { "name": "Sampler", "cpu_time_us": 4924.103, "cuda_time_us": 138.846, "pct_cuda_time": 1.9063852082763528, "trace": "" }, "children": [ { "entry": { "name": "Memcpy HtoD (Pinned -> Device)", "cpu_time_us": 0, "cuda_time_us": 0.735, "pct_cuda_time": 0.010091706841271041, "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 15, 0, None, None, True, False, None)" }, "children": [] }, { "entry": { "name": "Memcpy HtoD (Pinned -> Device)", "cpu_time_us": 0, "cuda_time_us": 0.704, "pct_cuda_time": 0.009666070226197025, "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 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": 0.010544803883124027, "trace": "copy_(int32[20], int32[20], True) <- _to_copy(int32[20], 3, 0, None, None, True, None) <- to(int32[20], 3, 0, None, None, True, False, None)" }, "children": [] }, { "entry": { "name": "Memcpy HtoD (Pinned -> Device)", "cpu_time_us": 0, "cuda_time_us": 0.8, "pct_cuda_time": 0.01098417071158753, "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 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": 0.010544803883124027, "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 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": 0.01098417071158753, "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 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": 0.010544803883124027, "trace": "copy_(bfloat16[20], bfloat16[20], True) <- _to_copy(bfloat16[20], 15, 0, None, None, True, None) <- to(bfloat16[20], 15, 0, None, None, True, False, None)" }, "children": [] }, { "entry": { "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", "cpu_time_us": 0, "cuda_time_us": 9.28, "pct_cuda_time": 0.12741638025441535, "trace": "copy_(float32[20, 128256], bfloat16[20, 128256], False) <- _to_copy(bfloat16[20, 128256], 6, None, None, None, False, None) <- to(bfloat16[20, 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": 13.216, "pct_cuda_time": 0.18145850015542597, "trace": "div_(float32[20, 128256], bfloat16[20, 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": 35.392, "pct_cuda_time": 0.4859397122806323, "trace": "_softmax(float32[20, 128256], -1, False) <- softmax(float32[20, 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": 29.791, "pct_cuda_time": 0.40903678708613006, "trace": "_log_softmax(float32[20, 128256], -1, False) <- log_softmax(float32[20, 128256], -1, 6)" }, "children": [] }, { "entry": { "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", "cpu_time_us": 0, "cuda_time_us": 2.112, "pct_cuda_time": 0.02899821067859108, "trace": "copy_(int64[20], int32[20], False) <- _to_copy(int32[20], 4, None, None, None, False, None) <- to(int32[20], 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": 12.992, "pct_cuda_time": 0.1783829323561815, "trace": "index(float32[20, 128256], None)" }, "children": [] }, { "entry": { "name": "void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, long, 4> >(at::native::ReduceOp, unsigned int, long, 4>)", "cpu_time_us": 0, "cuda_time_us": 28.223, "pct_cuda_time": 0.3875078124914185, "trace": "argmax(float32[20, 128256], -1, False)" }, "children": [] }, { "entry": { "name": "Memcpy DtoH (Device -> Pageable)", "cpu_time_us": 0, "cuda_time_us": 2.497, "pct_cuda_time": 0.03428434283354257, "trace": "copy_(int64[20], int64[20], False) <- _to_copy(int64[20], 4, 0, None, None, False, None) <- to(int64[20], 4, 0, None, None, False, False, None)" }, "children": [] } ] } ] } }