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at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1})", + "cuda_time_us": 10.368, + "pct_cuda_time": 0.013045900733378931, + "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": 37.248, + "pct_cuda_time": 0.04686860633843541, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::LogSoftMaxForwardEpilogue>(float*, float const*, int)", + "cuda_time_us": 30.048, + "pct_cuda_time": 0.037808953051366716, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, 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"RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 254.646, + "cuda_time_us": 58.463, + "pct_cuda_time": 0.0735631264058191, + "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": 58.463, + "pct_cuda_time": 0.0735631264058191, + "trace": "_C::rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2961.023, + "cuda_time_us": 544.565, + "pct_cuda_time": 0.68521806837119, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 384.328, + "cuda_time_us": 250.203, + "pct_cuda_time": 0.31482672658117367, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.767, + "pct_cuda_time": 0.0009651047321085687, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 249.436, + "pct_cuda_time": 0.3138616218490651, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 968.491, + "cuda_time_us": 46.879, + "pct_cuda_time": 0.058987150895068556, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 46.879, + "pct_cuda_time": 0.058987150895068556, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1038.853, + "cuda_time_us": 74.622, + "pct_cuda_time": 0.09389575660939453, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.328, + "pct_cuda_time": 0.024320135935064425, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.823, + "pct_cuda_time": 0.06772468317637483, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0018509374979552864, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 312.875, + "cuda_time_us": 172.86100000000002, + "pct_cuda_time": 0.2175084342855532, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0009248396063882632, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 172.126, + "pct_cuda_time": 0.2165835946791649, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 123.384, + "cuda_time_us": 36.031, + "pct_cuda_time": 0.04533727327588505, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.031, + "pct_cuda_time": 0.04533727327588505, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 592.86, + "cuda_time_us": 1820.873, + "pct_cuda_time": 2.2911775083034236, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 206.41, + "cuda_time_us": 1111.6670000000001, + "pct_cuda_time": 1.3987941098160839, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0009273561767457825, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1110.93, + "pct_cuda_time": 1.3978667536393379, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 139.144, + "cuda_time_us": 153.534, + "pct_cuda_time": 0.1931895566356675, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 153.534, + "pct_cuda_time": 0.1931895566356675, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 173.521, + "cuda_time_us": 555.672, + "pct_cuda_time": 0.6991938418516722, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 554.936, + "pct_cuda_time": 0.6982677439601053, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2594.453, + "cuda_time_us": 2434.8160000000003, + "pct_cuda_time": 3.063692885806593, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.319, + "cuda_time_us": 38.623, + "pct_cuda_time": 0.04859874845922978, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 38.623, + "pct_cuda_time": 0.04859874845922978, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1856.25, + "cuda_time_us": 537.0169999999999, + "pct_cuda_time": 0.6757205318419128, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 162.037, + "cuda_time_us": 242.429, + "pct_cuda_time": 0.305044817601497, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 241.693, + "pct_cuda_time": 0.30411871970993, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 540.799, + "cuda_time_us": 47.423, + "pct_cuda_time": 0.05967165803231376, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 47.423, + "pct_cuda_time": 0.05967165803231376, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 778.194, + "cuda_time_us": 74.431, + "pct_cuda_time": 0.09365542414025145, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.2, + "pct_cuda_time": 0.024159075432183204, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.919, + "pct_cuda_time": 0.06784547855353573, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0016508701545325192, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 213.116, + "cuda_time_us": 172.734, + "pct_cuda_time": 0.21734863206785074, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 171.966, + "pct_cuda_time": 0.21638226905056338, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.103, + "cuda_time_us": 35.744, + "pct_cuda_time": 0.04497614542958106, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 35.744, + "pct_cuda_time": 0.04497614542958106, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 486.154, + "cuda_time_us": 1823.4320000000002, + "pct_cuda_time": 2.294397460075869, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 167.611, + "cuda_time_us": 1113.681, + "pct_cuda_time": 1.4013282961661055, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1112.945, + "pct_cuda_time": 1.4004021982745383, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.853, + "cuda_time_us": 153.63, + "pct_cuda_time": 0.19331035201282842, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 153.63, + "pct_cuda_time": 0.19331035201282842, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 157.765, + "cuda_time_us": 556.121, + "pct_cuda_time": 0.6997588118969351, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 555.385, + "pct_cuda_time": 0.6988327140053682, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2594.276, + "cuda_time_us": 2440.768, + "pct_cuda_time": 3.0711821991905697, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 94.432, + "cuda_time_us": 38.079, + "pct_cuda_time": 0.047914241321984595, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 38.079, + "pct_cuda_time": 0.047914241321984595, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1821.903, + "cuda_time_us": 534.842, + "pct_cuda_time": 0.6729837615781109, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 157.91, + "cuda_time_us": 242.557, + "pct_cuda_time": 0.3052058781043782, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 241.821, + "pct_cuda_time": 0.30427978021281116, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 537.364, + "cuda_time_us": 47.36, + "pct_cuda_time": 0.059592386066051904, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 47.36, + "pct_cuda_time": 0.059592386066051904, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 757.579, + "cuda_time_us": 73.823, + "pct_cuda_time": 0.09289038675156566, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.295, + "pct_cuda_time": 0.024278612524165362, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.12, + "pct_cuda_time": 0.06684010869570686, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.001771665531693435, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 208.314, + "cuda_time_us": 171.102, + "pct_cuda_time": 0.21529511065611515, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 170.366, + "pct_cuda_time": 0.21436901276454812, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 88.018, + "cuda_time_us": 37.119, + "pct_cuda_time": 0.04670628755037544, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.119, + "pct_cuda_time": 0.04670628755037544, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 500.026, + "cuda_time_us": 1830.728, + "pct_cuda_time": 2.303577908740099, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 171.129, + "cuda_time_us": 1118.9940000000001, + "pct_cuda_time": 1.408013565320855, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1118.258, + "pct_cuda_time": 1.407087467429288, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.886, + "cuda_time_us": 153.566, + "pct_cuda_time": 0.1932298217613878, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 153.566, + "pct_cuda_time": 0.1932298217613878, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.077, + "cuda_time_us": 558.168, + "pct_cuda_time": 0.702334521657856, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 557.432, + "pct_cuda_time": 0.701408423766289, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2539.751, + "cuda_time_us": 2431.135, + "pct_cuda_time": 3.059061138063579, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.438, + "cuda_time_us": 37.695, + "pct_cuda_time": 0.047431059813340934, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.695, + "pct_cuda_time": 0.047431059813340934, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1804.457, + "cuda_time_us": 531.9929999999999, + "pct_cuda_time": 0.6693989071038249, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.618, + "cuda_time_us": 243.453, + "pct_cuda_time": 0.30633330162454675, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 242.717, + "pct_cuda_time": 0.30540720373297975, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 501.43, + "cuda_time_us": 46.879, + "pct_cuda_time": 0.058987150895068556, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 46.879, + "pct_cuda_time": 0.058987150895068556, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 767.278, + "cuda_time_us": 74.783, + "pct_cuda_time": 0.09409834052317483, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.168, + "pct_cuda_time": 0.024118810306462898, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 54.111, + "pct_cuda_time": 0.06808706930785756, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.001892460908854351, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 215.359, + "cuda_time_us": 166.878, + "pct_cuda_time": 0.2099801140610348, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 166.142, + "pct_cuda_time": 0.2090540161694678, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.476, + "cuda_time_us": 35.871, + "pct_cuda_time": 0.04513594764728353, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 35.871, + "pct_cuda_time": 0.04513594764728353, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 502.944, + "cuda_time_us": 1825.576, + "pct_cuda_time": 2.297095223499129, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 175.039, + "cuda_time_us": 1114.801, + "pct_cuda_time": 1.402737575566316, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1114.033, + "pct_cuda_time": 1.4017712125490287, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.289, + "cuda_time_us": 153.182, + "pct_cuda_time": 0.19274664025274413, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 153.182, + "pct_cuda_time": 0.19274664025274413, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.09, + "cuda_time_us": 557.593, + "pct_cuda_time": 0.7016110076800691, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 556.857, + "pct_cuda_time": 0.7006849097885022, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2497.578, + "cuda_time_us": 2428.384, + "pct_cuda_time": 3.0555995955368114, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.123, + "cuda_time_us": 37.247, + "pct_cuda_time": 0.04686734805325665, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.247, + "pct_cuda_time": 0.04686734805325665, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1790.874, + "cuda_time_us": 530.713, + "pct_cuda_time": 0.6677883020750127, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.932, + "cuda_time_us": 242.909, + "pct_cuda_time": 0.30564879448730153, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 242.173, + "pct_cuda_time": 0.30472269659573453, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 528.661, + "cuda_time_us": 47.423, + "pct_cuda_time": 0.05967165803231376, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 47.423, + "pct_cuda_time": 0.05967165803231376, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 754.767, + "cuda_time_us": 74.463, + "pct_cuda_time": 0.09369568926597176, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.296, + "pct_cuda_time": 0.024279870809344118, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.535, + "pct_cuda_time": 0.06736229704489206, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.632, + "pct_cuda_time": 0.0020535214117355723, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 201.146, + "cuda_time_us": 165.91799999999998, + "pct_cuda_time": 0.20877216028942566, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 165.182, + "pct_cuda_time": 0.20784606239785866, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.277, + "cuda_time_us": 36.287, + "pct_cuda_time": 0.045659394281647496, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.287, + "pct_cuda_time": 0.045659394281647496, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 467.458, + "cuda_time_us": 1824.1370000000002, + "pct_cuda_time": 2.295284551126895, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.843, + "cuda_time_us": 1113.842, + "pct_cuda_time": 1.4015308800798858, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1113.106, + "pct_cuda_time": 1.4006047821883187, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.157, + "cuda_time_us": 152.926, + "pct_cuda_time": 0.1924245192469817, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 152.926, + "pct_cuda_time": 0.1924245192469817, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.146, + "cuda_time_us": 557.369, + "pct_cuda_time": 0.7013291518000272, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 556.633, + "pct_cuda_time": 0.7004030539084601, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2723.794, + "cuda_time_us": 2438.2709999999997, + "pct_cuda_time": 3.0680402610992066, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.363, + "cuda_time_us": 37.44, + "pct_cuda_time": 0.047110197092757246, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.44, + "pct_cuda_time": 0.047110197092757246, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1984.737, + "cuda_time_us": 538.488, + "pct_cuda_time": 0.6775714693398682, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.892, + "cuda_time_us": 243.901, + "pct_cuda_time": 0.30689701338463105, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 243.133, + "pct_cuda_time": 0.3059306503673437, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 667.339, + "cuda_time_us": 47.103, + "pct_cuda_time": 0.059269006775110704, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 47.103, + "pct_cuda_time": 0.059269006775110704, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 767.217, + "cuda_time_us": 74.719, + "pct_cuda_time": 0.09401781027173421, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.712, + "pct_cuda_time": 0.024803317443708086, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.727, + "pct_cuda_time": 0.0676038877992139, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0016106050288122139, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 221.699, + "cuda_time_us": 172.76500000000001, + "pct_cuda_time": 0.21738763890839224, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 171.997, + "pct_cuda_time": 0.21642127589110494, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.032, + "cuda_time_us": 35.935, + "pct_cuda_time": 0.04521647789872414, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 35.935, + "pct_cuda_time": 0.04521647789872414, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 482.027, + "cuda_time_us": 1826.408, + "pct_cuda_time": 2.2981421167678575, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 177.275, + "cuda_time_us": 1116.369, + "pct_cuda_time": 1.4047105667266109, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0009248396063882632, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1115.634, + "pct_cuda_time": 1.4037857271202228, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.31, + "cuda_time_us": 152.733, + "pct_cuda_time": 0.19218167020748111, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 152.733, + "pct_cuda_time": 0.19218167020748111, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 152.718, + "cuda_time_us": 557.3059999999999, + "pct_cuda_time": 0.7012498798337652, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0009273561767457825, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 556.569, + "pct_cuda_time": 0.7003225236570194, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2542.913, + "cuda_time_us": 2433.284, + "pct_cuda_time": 3.061765192912733, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.366, + "cuda_time_us": 37.408, + "pct_cuda_time": 0.047069931967036946, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.408, + "pct_cuda_time": 0.047069931967036946, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1838.124, + "cuda_time_us": 533.818, + "pct_cuda_time": 0.6716952775550611, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 160.384, + "cuda_time_us": 243.03699999999998, + "pct_cuda_time": 0.30580985499018276, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 242.301, + "pct_cuda_time": 0.30488375709861576, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 529.847, + "cuda_time_us": 47.584, + "pct_cuda_time": 0.059874241946094045, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 47.584, + "pct_cuda_time": 0.059874241946094045, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 784.332, + "cuda_time_us": 74.08, + "pct_cuda_time": 0.09321376604250686, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.232, + "pct_cuda_time": 0.024199340557903508, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.536, + "pct_cuda_time": 0.06736355533007084, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0016508701545325192, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 209.634, + "cuda_time_us": 169.117, + "pct_cuda_time": 0.2127974145762774, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 168.381, + "pct_cuda_time": 0.21187131668471043, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.106, + "cuda_time_us": 36.192, + "pct_cuda_time": 0.04553985718966534, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.192, + "pct_cuda_time": 0.04553985718966534, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.116, + "cuda_time_us": 1825.866, + "pct_cuda_time": 2.2974601262009697, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.414, + "cuda_time_us": 1115.4750000000001, + "pct_cuda_time": 1.4035856597768002, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0009273561767457825, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1114.738, + "pct_cuda_time": 1.4026583036000544, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.006, + "cuda_time_us": 153.726, + "pct_cuda_time": 0.19343114738998932, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 153.726, + "pct_cuda_time": 0.19343114738998932, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 153.05, + "cuda_time_us": 556.665, + "pct_cuda_time": 0.7004433190341803, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.769, + "pct_cuda_time": 0.0009676213024660878, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 555.896, + "pct_cuda_time": 0.6994756977317143, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2440.746, + "cuda_time_us": 2430.335, + "pct_cuda_time": 3.0580545099205714, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.042, + "cuda_time_us": 37.984, + "pct_cuda_time": 0.04779470423000244, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.984, + "pct_cuda_time": 0.04779470423000244, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1754.146, + "cuda_time_us": 530.712, + "pct_cuda_time": 0.667787043789834, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 149.671, + "cuda_time_us": 243.164, + "pct_cuda_time": 0.3059696572078852, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 242.428, + "pct_cuda_time": 0.3050435593163182, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 527.378, + "cuda_time_us": 46.847, + "pct_cuda_time": 0.05894688576934826, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 46.847, + "pct_cuda_time": 0.05894688576934826, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 734.424, + "cuda_time_us": 74.71900000000001, + "pct_cuda_time": 0.09401781027173421, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.328, + "pct_cuda_time": 0.024320135935064425, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.887, + "pct_cuda_time": 0.06780521342781543, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.001892460908854351, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 200.346, + "cuda_time_us": 165.982, + "pct_cuda_time": 0.20885269054086628, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 165.246, + "pct_cuda_time": 0.20792659264929927, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.602, + "cuda_time_us": 36.863, + "pct_cuda_time": 0.04638416654461299, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.863, + "pct_cuda_time": 0.04638416654461299, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 459.458, + "cuda_time_us": 1824.776, + "pct_cuda_time": 2.296088595356122, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.771, + "cuda_time_us": 1112.5610000000001, + "pct_cuda_time": 1.3999190167658948, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1111.825, + "pct_cuda_time": 1.3989929188743278, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.416, + "cuda_time_us": 154.398, + "pct_cuda_time": 0.19427671503011573, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 154.398, + "pct_cuda_time": 0.19427671503011573, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 152.204, + "cuda_time_us": 557.817, + "pct_cuda_time": 0.7018928635601114, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 557.081, + "pct_cuda_time": 0.7009667656685443, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2529.289, + "cuda_time_us": 2430.656, + "pct_cuda_time": 3.0584584194629527, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.823, + "cuda_time_us": 38.527, + "pct_cuda_time": 0.04847795308206888, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 38.527, + "pct_cuda_time": 0.04847795308206888, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1724.561, + "cuda_time_us": 534.297, + "pct_cuda_time": 0.672297996155687, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 168.739, + "cuda_time_us": 244.126, + "pct_cuda_time": 0.3071801275498519, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0009273561767457825, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 243.389, + "pct_cuda_time": 0.30625277137310614, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 491.965, + "cuda_time_us": 47.615, + "pct_cuda_time": 0.059913248786635585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 47.615, + "pct_cuda_time": 0.059913248786635585, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 709.871, + "cuda_time_us": 74.911, + "pct_cuda_time": 0.09425940102605605, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.327, + "pct_cuda_time": 0.02431887764988567, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.76, + "pct_cuda_time": 0.06764541121011297, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.824, + "pct_cuda_time": 0.0022951121660574046, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 200.327, + "cuda_time_us": 167.64499999999998, + "pct_cuda_time": 0.21094521879314337, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 166.909, + "pct_cuda_time": 0.21001912090157637, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.179, + "cuda_time_us": 36.992, + "pct_cuda_time": 0.04654648533267297, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.992, + "pct_cuda_time": 0.04654648533267297, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 579.095, + "cuda_time_us": 1820.8400000000001, + "pct_cuda_time": 2.2911359848925246, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.735, + "cuda_time_us": 1109.777, + "pct_cuda_time": 1.3964159508282281, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1109.041, + "pct_cuda_time": 1.395489852936661, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.001, + "cuda_time_us": 153.31, + "pct_cuda_time": 0.19290770075562538, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 153.31, + "pct_cuda_time": 0.19290770075562538, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 271.885, + "cuda_time_us": 557.753, + "pct_cuda_time": 0.7018123333086709, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 557.017, + "pct_cuda_time": 0.7008862354171038, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2573.522, + "cuda_time_us": 2437.088, + "pct_cuda_time": 3.066551709732735, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.418, + "cuda_time_us": 36.991, + "pct_cuda_time": 0.04654522704749421, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.991, + "pct_cuda_time": 0.04654522704749421, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1803.13, + "cuda_time_us": 537.048, + "pct_cuda_time": 0.6757595386824544, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.008, + "cuda_time_us": 243.80499999999998, + "pct_cuda_time": 0.30677621800747007, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 243.069, + "pct_cuda_time": 0.30585012011590307, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 526.105, + "cuda_time_us": 46.623, + "pct_cuda_time": 0.05866502988930612, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 46.623, + "pct_cuda_time": 0.05866502988930612, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 748.495, + "cuda_time_us": 74.015, + "pct_cuda_time": 0.09313197750588749, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.232, + "pct_cuda_time": 0.024199340557903508, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.503, + "pct_cuda_time": 0.06732203191917177, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0016106050288122139, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 227.318, + "cuda_time_us": 172.60500000000002, + "pct_cuda_time": 0.21718631327979077, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0009248396063882632, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 171.87, + "pct_cuda_time": 0.21626147367340248, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.848, + "cuda_time_us": 35.968, + "pct_cuda_time": 0.04525800130962321, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 35.968, + "pct_cuda_time": 0.04525800130962321, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 513.057, + "cuda_time_us": 1827.0810000000001, + "pct_cuda_time": 2.2989889426931627, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 188.291, + "cuda_time_us": 1115.0910000000001, + "pct_cuda_time": 1.4031024782681565, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0009273561767457825, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1114.354, + "pct_cuda_time": 1.4021751220914105, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.815, + "cuda_time_us": 153.374, + "pct_cuda_time": 0.19298823100706597, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 153.374, + "pct_cuda_time": 0.19298823100706597, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 162.906, + "cuda_time_us": 558.616, + "pct_cuda_time": 0.7028982334179402, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0009248396063882632, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 557.881, + "pct_cuda_time": 0.701973393811552, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2468.511, + "cuda_time_us": 2431.969, + "pct_cuda_time": 3.0601105479026645, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.074, + "cuda_time_us": 37.216, + "pct_cuda_time": 0.04682834121271511, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.216, + "pct_cuda_time": 0.04682834121271511, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1766.779, + "cuda_time_us": 530.809, + "pct_cuda_time": 0.6679090974521736, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 157.865, + "cuda_time_us": 243.325, + "pct_cuda_time": 0.3061722411216655, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 242.557, + "pct_cuda_time": 0.3052058781043782, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 486.581, + "cuda_time_us": 47.039, + "pct_cuda_time": 0.05918847652367009, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 47.039, + "pct_cuda_time": 0.05918847652367009, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 760.706, + "cuda_time_us": 74.655, + "pct_cuda_time": 0.0939372800202936, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.296, + "pct_cuda_time": 0.024279870809344118, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.983, + "pct_cuda_time": 0.06792600880497635, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0017314004059731294, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 205.22, + "cuda_time_us": 165.79, + "pct_cuda_time": 0.20861109978654443, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 165.054, + "pct_cuda_time": 0.20768500189497743, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.486, + "cuda_time_us": 36.543, + "pct_cuda_time": 0.04598151528740994, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.543, + "pct_cuda_time": 0.04598151528740994, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 466.547, + "cuda_time_us": 1827.4010000000003, + "pct_cuda_time": 2.2993915939503657, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.646, + "cuda_time_us": 1115.0900000000001, + "pct_cuda_time": 1.4031012199829778, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1114.354, + "pct_cuda_time": 1.4021751220914105, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.525, + "cuda_time_us": 153.822, + "pct_cuda_time": 0.19355194276715026, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 153.822, + "pct_cuda_time": 0.19355194276715026, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.0, + "cuda_time_us": 558.489, + "pct_cuda_time": 0.7027384312002378, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.769, + "pct_cuda_time": 0.0009676213024660878, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 557.72, + "pct_cuda_time": 0.7017708098977717, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2571.706, + "cuda_time_us": 2440.385, + "pct_cuda_time": 3.0707002759671047, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.081, + "cuda_time_us": 38.143, + "pct_cuda_time": 0.04799477157342521, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 38.143, + "pct_cuda_time": 0.04799477157342521, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1837.28, + "cuda_time_us": 538.329, + "pct_cuda_time": 0.6773714019964454, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 152.788, + "cuda_time_us": 244.60399999999998, + "pct_cuda_time": 0.30778158786529897, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 243.868, + "pct_cuda_time": 0.30685548997373197, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 498.878, + "cuda_time_us": 48.064, + "pct_cuda_time": 0.06047821883189862, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 48.064, + "pct_cuda_time": 0.06047821883189862, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 728.28, + "cuda_time_us": 74.399, + "pct_cuda_time": 0.09361515901453116, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.392, + "pct_cuda_time": 0.024400666186505035, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.504, + "pct_cuda_time": 0.06732329020435053, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0018912026236755912, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 222.158, + "cuda_time_us": 171.262, + "pct_cuda_time": 0.21549643628471665, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 170.526, + "pct_cuda_time": 0.21457033839314965, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 90.16, + "cuda_time_us": 37.408, + "pct_cuda_time": 0.047069931967036946, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.408, + "pct_cuda_time": 0.047069931967036946, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 474.819, + "cuda_time_us": 1826.505, + "pct_cuda_time": 2.298264170430197, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 167.103, + "cuda_time_us": 1115.0890000000002, + "pct_cuda_time": 1.403099961697799, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1114.353, + "pct_cuda_time": 1.402173863806232, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.747, + "cuda_time_us": 153.855, + "pct_cuda_time": 0.19359346617804932, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 153.855, + "pct_cuda_time": 0.19359346617804932, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.847, + "cuda_time_us": 557.561, + "pct_cuda_time": 0.7015707425543489, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 556.793, + "pct_cuda_time": 0.7006043795370617, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2744.755, + "cuda_time_us": 2441.729, + "pct_cuda_time": 3.072391411247357, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.353, + "cuda_time_us": 38.399, + "pct_cuda_time": 0.04831689257918765, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 38.399, + "pct_cuda_time": 0.04831689257918765, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2018.529, + "cuda_time_us": 538.5559999999999, + "pct_cuda_time": 0.6776570327320238, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.283, + "cuda_time_us": 243.83599999999998, + "pct_cuda_time": 0.30681522484801166, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.767, + "pct_cuda_time": 0.0009651047321085687, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 243.069, + "pct_cuda_time": 0.30585012011590307, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 529.364, + "cuda_time_us": 47.584, + "pct_cuda_time": 0.059874241946094045, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 47.584, + "pct_cuda_time": 0.059874241946094045, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 969.621, + "cuda_time_us": 74.20899999999999, + "pct_cuda_time": 0.09337608483056682, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.136, + "pct_cuda_time": 0.02407854518074259, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.568, + "pct_cuda_time": 0.06740382045579113, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.505, + "pct_cuda_time": 0.0018937191940331104, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 213.583, + "cuda_time_us": 172.927, + "pct_cuda_time": 0.21759148110735127, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0009273561767457825, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 172.19, + "pct_cuda_time": 0.21666412493060555, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 102.95, + "cuda_time_us": 35.839, + "pct_cuda_time": 0.04509568252156322, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 35.839, + "pct_cuda_time": 0.04509568252156322, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 472.152, + "cuda_time_us": 1828.935, + "pct_cuda_time": 2.3013218034145826, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.962, + "cuda_time_us": 1116.2730000000001, + "pct_cuda_time": 1.4045897713494504, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1115.537, + "pct_cuda_time": 1.4036636734578831, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.157, + "cuda_time_us": 153.214, + "pct_cuda_time": 0.19278690537846446, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 153.214, + "pct_cuda_time": 0.19278690537846446, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 152.477, + "cuda_time_us": 559.448, + "pct_cuda_time": 0.7039451266866682, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.888, + "pct_cuda_time": 0.0023756424174980148, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 557.56, + "pct_cuda_time": 0.7015694842691701, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2409.999, + "cuda_time_us": 2430.142, + "pct_cuda_time": 3.0578116608810704, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.695, + "cuda_time_us": 37.471, + "pct_cuda_time": 0.047149203933298786, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.471, + "pct_cuda_time": 0.047149203933298786, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1742.124, + "cuda_time_us": 531.737, + "pct_cuda_time": 0.6690767860980624, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.124, + "cuda_time_us": 243.613, + "pct_cuda_time": 0.3065346272531483, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 242.877, + "pct_cuda_time": 0.3056085293615813, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 522.699, + "cuda_time_us": 47.135, + "pct_cuda_time": 0.059309271900831004, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 47.135, + "pct_cuda_time": 0.059309271900831004, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 720.4, + "cuda_time_us": 74.431, + "pct_cuda_time": 0.09365542414025145, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.616, + "pct_cuda_time": 0.024682522066547172, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.343, + "pct_cuda_time": 0.06712070629057025, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0018521957831340457, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 197.623, + "cuda_time_us": 166.558, + "pct_cuda_time": 0.2095774628038318, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 165.822, + "pct_cuda_time": 0.20865136491226474, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.81, + "cuda_time_us": 36.543, + "pct_cuda_time": 0.04598151528740994, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.543, + "pct_cuda_time": 0.04598151528740994, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 448.779, + "cuda_time_us": 1824.391, + "pct_cuda_time": 2.2956041555622995, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.011, + "cuda_time_us": 1112.977, + "pct_cuda_time": 1.4004424634002588, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.767, + "pct_cuda_time": 0.0009651047321085687, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1112.21, + "pct_cuda_time": 1.3994773586681502, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 91.563, + "cuda_time_us": 152.958, + "pct_cuda_time": 0.192464784372702, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 152.958, + "pct_cuda_time": 0.192464784372702, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 145.036, + "cuda_time_us": 558.456, + "pct_cuda_time": 0.7026969077893387, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 557.72, + "pct_cuda_time": 0.7017708098977717, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2560.612, + "cuda_time_us": 2422.593, + "pct_cuda_time": 3.048312866066615, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.514, + "cuda_time_us": 37.184, + "pct_cuda_time": 0.046788076086994805, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.184, + "pct_cuda_time": 0.046788076086994805, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1840.23, + "cuda_time_us": 526.073, + "pct_cuda_time": 0.6619498588455685, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.521, + "cuda_time_us": 241.789, + "pct_cuda_time": 0.30423951508709085, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 241.053, + "pct_cuda_time": 0.30331341719552385, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 486.73, + "cuda_time_us": 46.655, + "pct_cuda_time": 0.05870529501502643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 46.655, + "pct_cuda_time": 0.05870529501502643, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 823.657, + "cuda_time_us": 73.05499999999999, + "pct_cuda_time": 0.09192402373427833, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.04, + "pct_cuda_time": 0.023957749803581677, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 52.543, + "pct_cuda_time": 0.06611407814756261, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0018521957831340457, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 231.098, + "cuda_time_us": 164.57399999999998, + "pct_cuda_time": 0.20708102500917283, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 163.838, + "pct_cuda_time": 0.20615492711760583, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 90.014, + "cuda_time_us": 36.064, + "pct_cuda_time": 0.04537879668678412, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.064, + "pct_cuda_time": 0.04537879668678412, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 487.448, + "cuda_time_us": 1823.272, + "pct_cuda_time": 2.2941961344472674, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 171.806, + "cuda_time_us": 1113.201, + "pct_cuda_time": 1.400724319280301, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1112.465, + "pct_cuda_time": 1.3997982213887337, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.294, + "cuda_time_us": 153.566, + "pct_cuda_time": 0.1932298217613878, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 153.566, + "pct_cuda_time": 0.1932298217613878, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 156.035, + "cuda_time_us": 556.505, + "pct_cuda_time": 0.7002419934055789, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 555.769, + "pct_cuda_time": 0.6993158955140119, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2508.015, + "cuda_time_us": 2429.536, + "pct_cuda_time": 3.0570491400627424, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.373, + "cuda_time_us": 38.272, + "pct_cuda_time": 0.04815709036148518, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 38.272, + "pct_cuda_time": 0.04815709036148518, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1730.322, + "cuda_time_us": 530.361, + "pct_cuda_time": 0.6673453856920893, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 158.083, + "cuda_time_us": 243.581, + "pct_cuda_time": 0.306494362127428, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 242.845, + "pct_cuda_time": 0.305568264235861, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 501.517, + "cuda_time_us": 47.007, + "pct_cuda_time": 0.05914821139794979, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 47.007, + "pct_cuda_time": 0.05914821139794979, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 715.467, + "cuda_time_us": 73.759, + "pct_cuda_time": 0.09280985650012505, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.328, + "pct_cuda_time": 0.024320135935064425, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 52.927, + "pct_cuda_time": 0.06659725965620628, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.001892460908854351, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 200.367, + "cuda_time_us": 166.01399999999998, + "pct_cuda_time": 0.20889295566658656, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 165.278, + "pct_cuda_time": 0.20796685777501955, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.09, + "cuda_time_us": 36.991, + "pct_cuda_time": 0.04654522704749421, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.991, + "pct_cuda_time": 0.04654522704749421, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 522.663, + "cuda_time_us": 1823.912, + "pct_cuda_time": 2.2950014369616736, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.999, + "cuda_time_us": 1114.257, + "pct_cuda_time": 1.4020530684290708, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1113.489, + "pct_cuda_time": 1.4010867054117835, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 135.344, + "cuda_time_us": 153.31, + "pct_cuda_time": 0.19290770075562538, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 153.31, + "pct_cuda_time": 0.19290770075562538, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 173.411, + "cuda_time_us": 556.345, + "pct_cuda_time": 0.7000406677769774, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 555.577, + "pct_cuda_time": 0.69907430475969, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2535.721, + "cuda_time_us": 2435.5240000000003, + "pct_cuda_time": 3.0645837517131547, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.994, + "cuda_time_us": 38.432, + "pct_cuda_time": 0.048358415990086716, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 38.432, + "pct_cuda_time": 0.048358415990086716, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1782.034, + "cuda_time_us": 531.804, + "pct_cuda_time": 0.6691610912050394, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.837, + "cuda_time_us": 242.654, + "pct_cuda_time": 0.3053279317667179, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.769, + "pct_cuda_time": 0.0009676213024660878, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 241.885, + "pct_cuda_time": 0.30436031046425177, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 492.385, + "cuda_time_us": 47.168, + "pct_cuda_time": 0.05935079531173008, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 47.168, + "pct_cuda_time": 0.05935079531173008, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 760.723, + "cuda_time_us": 74.07900000000001, + "pct_cuda_time": 0.09321250775732812, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.231, + "pct_cuda_time": 0.02419808227272475, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.088, + "pct_cuda_time": 0.06679984356998656, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.76, + "pct_cuda_time": 0.002214581914616794, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 225.56, + "cuda_time_us": 167.903, + "pct_cuda_time": 0.21126985636926335, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0009273561767457825, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 167.166, + "pct_cuda_time": 0.21034250019251757, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.766, + "cuda_time_us": 36.896, + "pct_cuda_time": 0.04642568995551206, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.896, + "pct_cuda_time": 0.04642568995551206, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 515.285, + "cuda_time_us": 1828.3920000000003, + "pct_cuda_time": 2.3006385545625165, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 171.354, + "cuda_time_us": 1117.1370000000002, + "pct_cuda_time": 1.4056769297438985, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1116.401, + "pct_cuda_time": 1.4047508318523314, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.369, + "cuda_time_us": 153.502, + "pct_cuda_time": 0.1931492915099472, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 153.502, + "pct_cuda_time": 0.1931492915099472, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 176.46, + "cuda_time_us": 557.753, + "pct_cuda_time": 0.7018123333086709, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 556.985, + "pct_cuda_time": 0.7008459702913835, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2493.806, + "cuda_time_us": 2427.809, + "pct_cuda_time": 3.054876081559025, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.798, + "cuda_time_us": 38.4, + "pct_cuda_time": 0.04831815086436641, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 38.4, + "pct_cuda_time": 0.04831815086436641, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1753.955, + "cuda_time_us": 529.977, + "pct_cuda_time": 0.6668622041834457, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 154.06, + "cuda_time_us": 242.36499999999998, + "pct_cuda_time": 0.3049642873500564, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 241.629, + "pct_cuda_time": 0.30403818945848937, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 494.59, + "cuda_time_us": 47.2, + "pct_cuda_time": 0.05939106043745038, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 47.2, + "pct_cuda_time": 0.05939106043745038, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 742.643, + "cuda_time_us": 73.887, + "pct_cuda_time": 0.09297091700300628, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.199, + "pct_cuda_time": 0.024157817147004445, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.408, + "pct_cuda_time": 0.06720249482718961, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0016106050288122139, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 206.162, + "cuda_time_us": 166.52499999999998, + "pct_cuda_time": 0.2095359393929327, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 165.789, + "pct_cuda_time": 0.2086098415013657, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.542, + "cuda_time_us": 36.032, + "pct_cuda_time": 0.04533853156106381, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.032, + "pct_cuda_time": 0.04533853156106381, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 486.786, + "cuda_time_us": 1823.4, + "pct_cuda_time": 2.2943571949501487, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 167.575, + "cuda_time_us": 1113.074, + "pct_cuda_time": 1.4005645170625984, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1112.338, + "pct_cuda_time": 1.3996384191710314, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 106.743, + "cuda_time_us": 152.638, + "pct_cuda_time": 0.19206213311549897, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 152.638, + "pct_cuda_time": 0.19206213311549897, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 156.012, + "cuda_time_us": 557.688, + "pct_cuda_time": 0.7017305447720514, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 556.952, + "pct_cuda_time": 0.7008044468804844, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2468.642, + "cuda_time_us": 2440.993, + "pct_cuda_time": 3.07146531335579, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.858, + "cuda_time_us": 37.344, + "pct_cuda_time": 0.04698940171559633, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.344, + "pct_cuda_time": 0.04698940171559633, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1707.143, + "cuda_time_us": 530.585, + "pct_cuda_time": 0.6676272415721316, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.951, + "cuda_time_us": 243.101, + "pct_cuda_time": 0.3058903852416234, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 242.333, + "pct_cuda_time": 0.30492402222433607, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 474.982, + "cuda_time_us": 47.039, + "pct_cuda_time": 0.05918847652367009, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 47.039, + "pct_cuda_time": 0.05918847652367009, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 720.138, + "cuda_time_us": 74.111, + "pct_cuda_time": 0.09325277288304842, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.36, + "pct_cuda_time": 0.02436040106078473, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 53.439, + "pct_cuda_time": 0.06724150166773116, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0016508701545325192, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 211.866, + "cuda_time_us": 166.334, + "pct_cuda_time": 0.20929560692378968, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 165.598, + "pct_cuda_time": 0.20836950903222262, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 112.055, + "cuda_time_us": 35.968, + "pct_cuda_time": 0.04525800130962321, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 35.968, + "pct_cuda_time": 0.04525800130962321, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 500.187, + "cuda_time_us": 1837.096, + "pct_cuda_time": 2.3115906687584395, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 188.831, + "cuda_time_us": 1113.905, + "pct_cuda_time": 1.4016101520461475, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1113.137, + "pct_cuda_time": 1.40064378902886, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.55, + "cuda_time_us": 152.766, + "pct_cuda_time": 0.19222319361838017, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 152.766, + "pct_cuda_time": 0.19222319361838017, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.185, + "cuda_time_us": 570.4250000000001, + "pct_cuda_time": 0.7177573230939118, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 569.657, + "pct_cuda_time": 0.7167909600766245, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2830.089, + "cuda_time_us": 2514.239, + "pct_cuda_time": 3.163629669559212, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.211, + "cuda_time_us": 37.407, + "pct_cuda_time": 0.04706867368185818, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.407, + "pct_cuda_time": 0.04706867368185818, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2119.455, + "cuda_time_us": 558.7139999999999, + "pct_cuda_time": 0.7030215453654587, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 169.544, + "cuda_time_us": 253.213, + "pct_cuda_time": 0.3186141649692399, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 252.477, + "pct_cuda_time": 0.3176880670776729, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 536.678, + "cuda_time_us": 48.511, + "pct_cuda_time": 0.06104067230680414, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 48.511, + "pct_cuda_time": 0.06104067230680414, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1038.557, + "cuda_time_us": 76.92699999999999, + "pct_cuda_time": 0.09679610394643527, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.616, + "pct_cuda_time": 0.024682522066547172, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 55.775, + "pct_cuda_time": 0.07018085584531344, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.536, + "pct_cuda_time": 0.0019327260345746566, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 220.939, + "cuda_time_us": 180.063, + "pct_cuda_time": 0.2265706041429794, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0009273561767457825, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 179.326, + "pct_cuda_time": 0.22564324796623358, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.397, + "cuda_time_us": 36.639, + "pct_cuda_time": 0.046102310664570864, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.639, + "pct_cuda_time": 0.046102310664570864, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 469.187, + "cuda_time_us": 1881.479, + "pct_cuda_time": 2.367437139847324, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.219, + "cuda_time_us": 1151.729, + "pct_cuda_time": 1.4492035306475484, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1150.993, + "pct_cuda_time": 1.4482774327559813, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.498, + "cuda_time_us": 155.998, + "pct_cuda_time": 0.19628997131613102, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 155.998, + "pct_cuda_time": 0.19628997131613102, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.805, + "cuda_time_us": 573.752, + "pct_cuda_time": 0.7219436378836446, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 573.016, + "pct_cuda_time": 0.7210175399920776, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2532.447, + "cuda_time_us": 2504.1919999999996, + "pct_cuda_time": 3.150987678368214, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.089, + "cuda_time_us": 39.136, + "pct_cuda_time": 0.04924424875593343, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 39.136, + "pct_cuda_time": 0.04924424875593343, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1814.469, + "cuda_time_us": 552.2819999999999, + "pct_cuda_time": 0.6949282550956772, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 151.592, + "cuda_time_us": 252.796, + "pct_cuda_time": 0.3180894600496971, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 252.028, + "pct_cuda_time": 0.3171230970324098, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 509.437, + "cuda_time_us": 48.896, + "pct_cuda_time": 0.061525112100626556, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 48.896, + "pct_cuda_time": 0.061525112100626556, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 768.865, + "cuda_time_us": 77.503, + "pct_cuda_time": 0.09752087620940078, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.775, + "pct_cuda_time": 0.024882589409969937, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 56.064, + "pct_cuda_time": 0.07054450026197495, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.664, + "pct_cuda_time": 0.0020937865374558774, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 229.433, + "cuda_time_us": 173.087, + "pct_cuda_time": 0.2177928067359528, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0009273561767457825, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 172.35, + "pct_cuda_time": 0.21686545055920703, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.716, + "cuda_time_us": 36.992, + "pct_cuda_time": 0.04654648533267297, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.992, + "pct_cuda_time": 0.04654648533267297, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 478.163, + "cuda_time_us": 1875.7819999999997, + "pct_cuda_time": 2.3602686891839304, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 167.179, + "cuda_time_us": 1147.7279999999998, + "pct_cuda_time": 1.4441691316473313, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.0009248396063882632, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1146.993, + "pct_cuda_time": 1.4432442920409432, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.328, + "cuda_time_us": 155.454, + "pct_cuda_time": 0.19560546417888583, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 155.454, + "pct_cuda_time": 0.19560546417888583, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.096, + "cuda_time_us": 572.6, + "pct_cuda_time": 0.7204940933577137, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 571.864, + "pct_cuda_time": 0.7195679954661467, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2491.145, + "cuda_time_us": 2508.8959999999997, + "pct_cuda_time": 3.1569066518490994, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.098, + "cuda_time_us": 38.079, + "pct_cuda_time": 0.047914241321984595, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 38.079, + "pct_cuda_time": 0.047914241321984595, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1791.01, + "cuda_time_us": 552.3449999999999, + "pct_cuda_time": 0.695007527061939, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 172.224, + "cuda_time_us": 253.85299999999998, + "pct_cuda_time": 0.31941946748364597, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 253.117, + "pct_cuda_time": 0.3184933695920789, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 553.141, + "cuda_time_us": 48.511, + "pct_cuda_time": 0.06104067230680414, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 48.511, + "pct_cuda_time": 0.06104067230680414, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 712.645, + "cuda_time_us": 76.991, + "pct_cuda_time": 0.0968766341978759, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 20.096, + "pct_cuda_time": 0.025286498952351754, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 55.455, + "pct_cuda_time": 0.06977820458811039, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0018119306574137402, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 200.075, + "cuda_time_us": 172.98999999999998, + "pct_cuda_time": 0.2176707530736131, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 172.254, + "pct_cuda_time": 0.21674465518204614, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.115, + "cuda_time_us": 35.552, + "pct_cuda_time": 0.04473455467525923, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 35.552, + "pct_cuda_time": 0.04473455467525923, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 467.095, + "cuda_time_us": 1882.92, + "pct_cuda_time": 2.3692503287899167, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 162.603, + "cuda_time_us": 1149.3300000000002, + "pct_cuda_time": 1.4461849045037045, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0009273561767457825, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1148.593, + "pct_cuda_time": 1.4452575483269587, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.121, + "cuda_time_us": 155.806, + "pct_cuda_time": 0.1960483805618092, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 155.806, + "pct_cuda_time": 0.1960483805618092, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.88, + "cuda_time_us": 577.784, + "pct_cuda_time": 0.7270170437244031, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 577.048, + "pct_cuda_time": 0.7260909458328362, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2434.587, + "cuda_time_us": 2500.35, + "pct_cuda_time": 3.14615334671142, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.482, + "cuda_time_us": 38.559, + "pct_cuda_time": 0.04851821820778918, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 38.559, + "pct_cuda_time": 0.04851821820778918, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1758.238, + "cuda_time_us": 552.184, + "pct_cuda_time": 0.6948049431481589, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 173.836, + "cuda_time_us": 254.013, + "pct_cuda_time": 0.3196207931122475, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 253.245, + "pct_cuda_time": 0.3186544300949602, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 540.433, + "cuda_time_us": 48.543, + "pct_cuda_time": 0.06108093743252444, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 48.543, + "pct_cuda_time": 0.06108093743252444, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 700.532, + "cuda_time_us": 76.959, + "pct_cuda_time": 0.09683636907215559, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.551, + "pct_cuda_time": 0.0246007335299278, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 55.904, + "pct_cuda_time": 0.07034317463337343, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.001892460908854351, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 197.76, + "cuda_time_us": 172.66899999999998, + "pct_cuda_time": 0.21726684353123132, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 171.933, + "pct_cuda_time": 0.21634074563966432, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.965, + "cuda_time_us": 37.024, + "pct_cuda_time": 0.04658675045839328, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.024, + "pct_cuda_time": 0.04658675045839328, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 455.525, + "cuda_time_us": 1872.583, + "pct_cuda_time": 2.3562434348970793, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.955, + "cuda_time_us": 1142.449, + "pct_cuda_time": 1.43752664418866, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1141.681, + "pct_cuda_time": 1.4365602811713725, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.082, + "cuda_time_us": 156.158, + "pct_cuda_time": 0.19649129694473255, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 156.158, + "pct_cuda_time": 0.19649129694473255, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.049, + "cuda_time_us": 573.976, + "pct_cuda_time": 0.7222254937636868, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 573.24, + "pct_cuda_time": 0.7212993958721198, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2576.813, + "cuda_time_us": 2518.1130000000003, + "pct_cuda_time": 3.168504266341727, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.505, + "cuda_time_us": 37.792, + "pct_cuda_time": 0.04755311347568061, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.792, + "pct_cuda_time": 0.04755311347568061, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1855.644, + "cuda_time_us": 559.706, + "pct_cuda_time": 0.7042697642627882, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 170.379, + "cuda_time_us": 253.50099999999998, + "pct_cuda_time": 0.3189765511007226, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 252.765, + "pct_cuda_time": 0.3180504532091556, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 531.839, + "cuda_time_us": 47.935, + "pct_cuda_time": 0.06031590004383865, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 47.935, + "pct_cuda_time": 0.06031590004383865, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 781.076, + "cuda_time_us": 78.24000000000001, + "pct_cuda_time": 0.09844823238614657, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 20.096, + "pct_cuda_time": 0.025286498952351754, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 56.447, + "pct_cuda_time": 0.07102642348543987, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.697, + "pct_cuda_time": 0.0021353099483549425, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 214.694, + "cuda_time_us": 180.03, + "pct_cuda_time": 0.2265290807320803, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 179.294, + "pct_cuda_time": 0.22560298284051333, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.596, + "cuda_time_us": 36.351, + "pct_cuda_time": 0.04573992453308811, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.351, + "pct_cuda_time": 0.04573992453308811, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 479.082, + "cuda_time_us": 1884.2640000000001, + "pct_cuda_time": 2.3709414640701696, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.532, + "cuda_time_us": 1155.025, + "pct_cuda_time": 1.45335083859674, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1154.289, + "pct_cuda_time": 1.4524247407051727, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.839, + "cuda_time_us": 155.582, + "pct_cuda_time": 0.19576652468176703, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 155.582, + "pct_cuda_time": 0.19576652468176703, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 153.45, + "cuda_time_us": 573.657, + "pct_cuda_time": 0.7218241007916626, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 572.921, + "pct_cuda_time": 0.7208980029000955, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2429.602, + "cuda_time_us": 2502.4620000000004, + "pct_cuda_time": 3.148810845008961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.02, + "cuda_time_us": 38.079, + "pct_cuda_time": 0.047914241321984595, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 38.079, + "pct_cuda_time": 0.047914241321984595, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1746.491, + "cuda_time_us": 551.577, + "pct_cuda_time": 0.6940411640446519, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 160.028, + "cuda_time_us": 253.085, + "pct_cuda_time": 0.31845310446635866, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 252.317, + "pct_cuda_time": 0.31748674144907135, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 507.875, + "cuda_time_us": 48.447, + "pct_cuda_time": 0.06096014205536353, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 48.447, + "pct_cuda_time": 0.06096014205536353, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 725.368, + "cuda_time_us": 76.767, + "pct_cuda_time": 0.09659477831783375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.424, + "pct_cuda_time": 0.02444093131222534, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 55.615, + "pct_cuda_time": 0.06997953021671192, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.728, + "pct_cuda_time": 0.0021743167888964884, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 195.102, + "cuda_time_us": 173.278, + "pct_cuda_time": 0.21803313920509587, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 172.51, + "pct_cuda_time": 0.21706677618780856, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.442, + "cuda_time_us": 36.575, + "pct_cuda_time": 0.046021780413130244, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.575, + "pct_cuda_time": 0.046021780413130244, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 458.786, + "cuda_time_us": 1876.2310000000002, + "pct_cuda_time": 2.3608336592291943, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.856, + "cuda_time_us": 1142.4170000000001, + "pct_cuda_time": 1.4374863790629397, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1141.681, + "pct_cuda_time": 1.4365602811713725, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.597, + "cuda_time_us": 155.39, + "pct_cuda_time": 0.1955249339274452, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 155.39, + "pct_cuda_time": 0.1955249339274452, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.014, + "cuda_time_us": 578.424, + "pct_cuda_time": 0.7278223462388093, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 577.688, + "pct_cuda_time": 0.7268962483472422, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2392.861, + "cuda_time_us": 2498.4950000000003, + "pct_cuda_time": 3.1438192277048223, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.502, + "cuda_time_us": 37.471, + "pct_cuda_time": 0.047149203933298786, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.471, + "pct_cuda_time": 0.047149203933298786, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1705.416, + "cuda_time_us": 550.265, + "pct_cuda_time": 0.6923902938901193, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.147, + "cuda_time_us": 252.541, + "pct_cuda_time": 0.31776859732911344, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 251.805, + "pct_cuda_time": 0.3168424994375465, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 500.598, + "cuda_time_us": 47.807, + "pct_cuda_time": 0.060154839540957426, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 47.807, + "pct_cuda_time": 0.060154839540957426, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 717.205, + "cuda_time_us": 77.055, + "pct_cuda_time": 0.09695716444931651, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.68, + "pct_cuda_time": 0.024763052317987783, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 55.871, + "pct_cuda_time": 0.07030165122247437, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.001892460908854351, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 198.63, + "cuda_time_us": 172.862, + "pct_cuda_time": 0.21750969257073194, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 172.126, + "pct_cuda_time": 0.2165835946791649, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.022, + "cuda_time_us": 36.383, + "pct_cuda_time": 0.045780189658808416, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.383, + "pct_cuda_time": 0.045780189658808416, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 455.642, + "cuda_time_us": 1874.3760000000002, + "pct_cuda_time": 2.3584995402225957, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.524, + "cuda_time_us": 1142.1930000000002, + "pct_cuda_time": 1.4372045231828976, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1141.457, + "pct_cuda_time": 1.4362784252913305, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.666, + "cuda_time_us": 155.294, + "pct_cuda_time": 0.19540413855028432, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 155.294, + "pct_cuda_time": 0.19540413855028432, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.881, + "cuda_time_us": 576.889, + "pct_cuda_time": 0.7258908784894134, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 576.153, + "pct_cuda_time": 0.7249647805978463, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2762.297, + "cuda_time_us": 2506.622, + "pct_cuda_time": 3.1540453113526006, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.206, + "cuda_time_us": 38.56, + "pct_cuda_time": 0.048519476492967936, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 38.56, + "pct_cuda_time": 0.048519476492967936, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2050.778, + "cuda_time_us": 553.752, + "pct_cuda_time": 0.6967779343084538, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 157.9, + "cuda_time_us": 253.69299999999998, + "pct_cuda_time": 0.31921814185504443, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 252.957, + "pct_cuda_time": 0.31829204396347743, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 562.454, + "cuda_time_us": 48.671, + "pct_cuda_time": 0.06124199793540566, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 48.671, + "pct_cuda_time": 0.06124199793540566, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 932.74, + "cuda_time_us": 76.863, + "pct_cuda_time": 0.09671557369499466, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.648, + "pct_cuda_time": 0.02472278719226748, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 55.871, + "pct_cuda_time": 0.07030165122247437, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.344, + "pct_cuda_time": 0.0016911352802528245, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 251.419, + "cuda_time_us": 174.52499999999998, + "pct_cuda_time": 0.219602220823009, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 173.789, + "pct_cuda_time": 0.218676122931442, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.938, + "cuda_time_us": 36.448, + "pct_cuda_time": 0.04586197819542778, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.448, + "pct_cuda_time": 0.04586197819542778, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 481.026, + "cuda_time_us": 1877.8619999999999, + "pct_cuda_time": 2.362885922355751, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 169.659, + "cuda_time_us": 1149.904, + "pct_cuda_time": 1.4469071601963122, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1149.136, + "pct_cuda_time": 1.445940797179025, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.108, + "cuda_time_us": 155.581, + "pct_cuda_time": 0.19576526639658826, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 155.581, + "pct_cuda_time": 0.19576526639658826, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.031, + "cuda_time_us": 572.377, + "pct_cuda_time": 0.7202134957628502, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 571.641, + "pct_cuda_time": 0.7192873978712833, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2489.849, + "cuda_time_us": 2504.5170000000003, + "pct_cuda_time": 3.151396621051312, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.116, + "cuda_time_us": 38.08, + "pct_cuda_time": 0.047915499607163355, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 38.08, + "pct_cuda_time": 0.047915499607163355, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1797.87, + "cuda_time_us": 553.403, + "pct_cuda_time": 0.6963387927810668, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 149.838, + "cuda_time_us": 254.237, + "pct_cuda_time": 0.31990264899228965, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 253.469, + "pct_cuda_time": 0.31893628597500234, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 553.505, + "cuda_time_us": 48.16, + "pct_cuda_time": 0.06059901420905953, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 48.16, + "pct_cuda_time": 0.06059901420905953, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 739.181, + "cuda_time_us": 77.18299999999999, + "pct_cuda_time": 0.09711822495219771, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.647, + "pct_cuda_time": 0.02472152890708872, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 56.0, + "pct_cuda_time": 0.07046397001053434, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.536, + "pct_cuda_time": 0.0019327260345746566, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 209.97, + "cuda_time_us": 173.823, + "pct_cuda_time": 0.21871890462751986, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0009273561767457825, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 173.086, + "pct_cuda_time": 0.21779154845077406, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.497, + "cuda_time_us": 36.832, + "pct_cuda_time": 0.04634515970407145, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.832, + "pct_cuda_time": 0.04634515970407145, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 466.778, + "cuda_time_us": 1876.2020000000002, + "pct_cuda_time": 2.36079716895901, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 162.391, + "cuda_time_us": 1148.275, + "pct_cuda_time": 1.444857413640113, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0009273561767457825, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1147.538, + "pct_cuda_time": 1.4439300574633673, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.381, + "cuda_time_us": 155.102, + "pct_cuda_time": 0.19516254779596248, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 155.102, + "pct_cuda_time": 0.19516254779596248, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 150.651, + "cuda_time_us": 572.825, + "pct_cuda_time": 0.7207772075229346, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 572.089, + "pct_cuda_time": 0.7198511096313676, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2461.478, + "cuda_time_us": 2503.679, + "pct_cuda_time": 3.1503421780715115, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.713, + "cuda_time_us": 37.663, + "pct_cuda_time": 0.04739079468762062, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.663, + "pct_cuda_time": 0.04739079468762062, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1754.755, + "cuda_time_us": 550.905, + "pct_cuda_time": 0.6931955964045254, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 147.097, + "cuda_time_us": 251.99699999999999, + "pct_cuda_time": 0.31708409019186823, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 251.261, + "pct_cuda_time": 0.3161579923003012, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 514.42, + "cuda_time_us": 48.447, + "pct_cuda_time": 0.06096014205536353, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 48.447, + "pct_cuda_time": 0.06096014205536353, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 726.899, + "cuda_time_us": 77.343, + "pct_cuda_time": 0.09731955058079926, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.744, + "pct_cuda_time": 0.024843582569428396, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 55.871, + "pct_cuda_time": 0.07030165122247437, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.728, + "pct_cuda_time": 0.0021743167888964884, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 223.092, + "cuda_time_us": 173.118, + "pct_cuda_time": 0.2178318135764944, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 172.382, + "pct_cuda_time": 0.21690571568492734, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.414, + "cuda_time_us": 36.831, + "pct_cuda_time": 0.04634390141889269, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.831, + "pct_cuda_time": 0.04634390141889269, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 475.743, + "cuda_time_us": 1878.2800000000002, + "pct_cuda_time": 2.3634118855604727, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 166.935, + "cuda_time_us": 1146.258, + "pct_cuda_time": 1.442319452434555, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1145.49, + "pct_cuda_time": 1.4413530894172677, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.657, + "cuda_time_us": 155.325, + "pct_cuda_time": 0.19544314539082583, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 155.325, + "pct_cuda_time": 0.19544314539082583, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.295, + "cuda_time_us": 576.697, + "pct_cuda_time": 0.7256492877350915, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 575.961, + "pct_cuda_time": 0.7247231898435246, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2546.448, + "cuda_time_us": 2496.542, + "pct_cuda_time": 3.1413617967507044, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.2, + "cuda_time_us": 38.847, + "pct_cuda_time": 0.048880604339271924, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 38.847, + "pct_cuda_time": 0.048880604339271924, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1830.538, + "cuda_time_us": 549.9449999999999, + "pct_cuda_time": 0.6919876426329162, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.307, + "cuda_time_us": 252.31699999999998, + "pct_cuda_time": 0.3174867414490713, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 251.581, + "pct_cuda_time": 0.3165606435575043, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 510.466, + "cuda_time_us": 48.607, + "pct_cuda_time": 0.061161467683965055, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 48.607, + "pct_cuda_time": 0.061161467683965055, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 816.578, + "cuda_time_us": 76.67099999999999, + "pct_cuda_time": 0.09647398294067283, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.84, + "pct_cuda_time": 0.02496437794658931, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 55.519, + "pct_cuda_time": 0.069858734839551, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0016508701545325192, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 205.438, + "cuda_time_us": 172.35, + "pct_cuda_time": 0.21686545055920703, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 171.614, + "pct_cuda_time": 0.21593935266764003, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.737, + "cuda_time_us": 37.183, + "pct_cuda_time": 0.046786817801816045, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.183, + "pct_cuda_time": 0.046786817801816045, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 485.701, + "cuda_time_us": 1870.567, + "pct_cuda_time": 2.3537067319767, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 183.794, + "cuda_time_us": 1142.545, + "pct_cuda_time": 1.437647439565821, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1141.809, + "pct_cuda_time": 1.4367213416742537, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.43, + "cuda_time_us": 155.518, + "pct_cuda_time": 0.19568599443032644, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 155.518, + "pct_cuda_time": 0.19568599443032644, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 149.556, + "cuda_time_us": 572.504, + "pct_cuda_time": 0.7203732979805527, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 571.768, + "pct_cuda_time": 0.7194472000889858, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2411.194, + "cuda_time_us": 2496.417, + "pct_cuda_time": 3.1412045111033593, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.999, + "cuda_time_us": 37.632, + "pct_cuda_time": 0.04735178784707907, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.632, + "pct_cuda_time": 0.04735178784707907, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1703.364, + "cuda_time_us": 552.0889999999999, + "pct_cuda_time": 0.6946854060561767, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 154.934, + "cuda_time_us": 253.628, + "pct_cuda_time": 0.31913635331842505, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 252.892, + "pct_cuda_time": 0.31821025542685805, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 493.834, + "cuda_time_us": 48.159, + "pct_cuda_time": 0.06059775592388078, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 48.159, + "pct_cuda_time": 0.06059775592388078, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 706.916, + "cuda_time_us": 77.24799999999999, + "pct_cuda_time": 0.09720001348881709, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 19.871, + "pct_cuda_time": 0.025003384787130857, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 56.032, + "pct_cuda_time": 0.07050423513625464, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.345, + "pct_cuda_time": 0.0016923935654315839, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 201.898, + "cuda_time_us": 173.054, + "pct_cuda_time": 0.21775128332505375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 172.318, + "pct_cuda_time": 0.21682518543348675, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.117, + "cuda_time_us": 36.448, + "pct_cuda_time": 0.04586197819542778, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.448, + "pct_cuda_time": 0.04586197819542778, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 463.824, + "cuda_time_us": 1870.248, + "pct_cuda_time": 2.3533053390046756, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.52, + "cuda_time_us": 1140.881, + "pct_cuda_time": 1.435553653028365, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1140.145, + "pct_cuda_time": 1.434627555136798, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.652, + "cuda_time_us": 155.326, + "pct_cuda_time": 0.1954444036760046, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 155.326, + "pct_cuda_time": 0.1954444036760046, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.123, + "cuda_time_us": 574.0409999999999, + "pct_cuda_time": 0.7223072823003062, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 573.305, + "pct_cuda_time": 0.7213811844087391, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2525.089, + "cuda_time_us": 2496.511, + "pct_cuda_time": 3.141322789910163, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.518, + "cuda_time_us": 37.6, + "pct_cuda_time": 0.04731152272135877, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 37.6, + "pct_cuda_time": 0.04731152272135877, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1800.674, + "cuda_time_us": 553.367, + "pct_cuda_time": 0.6962934945146314, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.621, + "cuda_time_us": 253.053, + "pct_cuda_time": 0.31841283934063835, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 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": 252.317, + "pct_cuda_time": 0.31748674144907135, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[3584, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 544.333, + "cuda_time_us": 48.639, + "pct_cuda_time": 0.061201732809685355, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 48.639, + "pct_cuda_time": 0.061201732809685355, + "trace": "_C::rotary_embedding(int64[3584], bfloat16[3584, 4096], bfloat16[3584, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 755.704, + "cuda_time_us": 77.598, + "pct_cuda_time": 0.09764041330138293, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 20.159, + "pct_cuda_time": 0.025365770918613605, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[3584], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 55.904, + "pct_cuda_time": 0.07034317463337343, + "trace": "_vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.535, + "pct_cuda_time": 0.0019314677493958965, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], None, None, bfloat16[3584, 32, 128], int32[15], int32[15], None, None, None, 256, 256, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[3584, 32, 128], bfloat16[3584, 8, 128], bfloat16[3584, 8, 128], bfloat16[3584, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 201.066, + "cuda_time_us": 174.077, + "pct_cuda_time": 0.21903850906292477, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 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": 173.341, + "pct_cuda_time": 0.21811241117135777, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[3584, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 98.39, + "cuda_time_us": 36.256, + "pct_cuda_time": 0.045620387441105956, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 36.256, + "pct_cuda_time": 0.045620387441105956, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 478.639, + "cuda_time_us": 1869.288, + "pct_cuda_time": 2.3520973852330664, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 179.331, + "cuda_time_us": 1139.633, + "pct_cuda_time": 1.433983313125273, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 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": 1138.865, + "pct_cuda_time": 1.4330169501079857, + "trace": "mm(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[3584, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[3584, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.202, + "cuda_time_us": 155.006, + "pct_cuda_time": 0.19504175241880156, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 155.006, + "pct_cuda_time": 0.19504175241880156, + "trace": "_C::silu_and_mul(bfloat16[3584, 14336], bfloat16[3584, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 147.812, + "cuda_time_us": 574.649, + "pct_cuda_time": 0.723072319688992, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0009663630172873283, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 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": 573.881, + "pct_cuda_time": 0.7221059566717046, + "trace": "mm(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[3584, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[3584, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.881, + "cuda_time_us": 38.176, + "pct_cuda_time": 0.048036294984324275, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 38.176, + "pct_cuda_time": 0.048036294984324275, + "trace": "_C::fused_add_rms_norm(bfloat16[3584, 4096], bfloat16[3584, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 471.15, + "cuda_time_us": 365.915, + "pct_cuda_time": 0.46042542118579777, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 10.016, + "pct_cuda_time": 0.012602984350455572, + "trace": "index_select(bfloat16[3584, 4096], 0, int64[14])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.0009260978915670229, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[14, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 355.163, + "pct_cuda_time": 0.44689633894377523, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[14, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 3971.949, + "cuda_time_us": 149.407, + "pct_cuda_time": 0.1879966137029269, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.003986247446310229, + "trace": "copy_(bfloat16[14], bfloat16[14], True) <- _to_copy(bfloat16[14], 15, 0, None, None, True, None) <- to(bfloat16[14], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.432, + "pct_cuda_time": 0.0030601495547432057, + "trace": "copy_(bfloat16[14], bfloat16[14], True) <- _to_copy(bfloat16[14], 15, 0, None, None, True, None) <- to(bfloat16[14], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.528, + "pct_cuda_time": 0.0031809449319041222, + "trace": "copy_(int32[14], int32[14], True) <- _to_copy(int32[14], 3, 0, None, None, True, None) <- to(int32[14], 3, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.496, + "pct_cuda_time": 0.0031406798061838167, + "trace": "copy_(bfloat16[14], bfloat16[14], True) <- _to_copy(bfloat16[14], 15, 0, None, None, True, None) <- to(bfloat16[14], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.56, + "pct_cuda_time": 0.0032212100576244278, + "trace": "copy_(bfloat16[14], bfloat16[14], True) <- _to_copy(bfloat16[14], 15, 0, None, None, True, None) <- to(bfloat16[14], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.463, + "pct_cuda_time": 0.0030991563952847516, + "trace": "copy_(bfloat16[14], bfloat16[14], True) <- _to_copy(bfloat16[14], 15, 0, None, None, True, None) <- to(bfloat16[14], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 2.465, + "pct_cuda_time": 0.0031016729656422708, + "trace": "copy_(bfloat16[14], bfloat16[14], True) <- _to_copy(bfloat16[14], 15, 0, None, None, True, None) <- to(bfloat16[14], 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": 7.328, + "pct_cuda_time": 0.009220713789949923, + "trace": "copy_(float32[14, 128256], bfloat16[14, 128256], False) <- _to_copy(bfloat16[14, 128256], 6, None, None, None, False, None) <- to(bfloat16[14, 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": 10.368, + "pct_cuda_time": 0.013045900733378931, + "trace": "div_(float32[14, 128256], bfloat16[14, 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": 37.248, + "pct_cuda_time": 0.04686860633843541, + "trace": "_softmax(float32[14, 128256], -1, False) <- softmax(float32[14, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::LogSoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 30.048, + "pct_cuda_time": 0.037808953051366716, + "trace": "_log_softmax(float32[14, 128256], -1, False) <- log_softmax(float32[14, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 1.888, + "pct_cuda_time": 0.0023756424174980148, + "trace": "copy_(int64[14], int32[14], False) <- _to_copy(int32[14], 4, None, None, None, False, None) <- to(int32[14], 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": 10.784, + "pct_cuda_time": 0.013569347367742901, + "trace": "index(float32[14, 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": 30.079, + "pct_cuda_time": 0.03784795989190826, + "trace": "argmax(float32[14, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 3.552, + "pct_cuda_time": 0.004469428954953893, + "trace": "copy_(int64[14], int64[14], False) <- _to_copy(int64[14], 4, 0, None, None, False, None) <- to(int64[14], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + }, + "decode_1": { + "metadata": { + "num_running_seqs": 14 + }, + "summary_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cuda_time_us": 6455.811, + "pct_cuda_time": 93.10397491772402, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cuda_time_us": 11.2, + "pct_cuda_time": 0.16152339637553037, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cuda_time_us": 11.2, + "pct_cuda_time": 0.16152339637553037, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cuda_time_us": 6441.4749999999985, + "pct_cuda_time": 92.89722497036333, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cuda_time_us": 203.13100000000009, + "pct_cuda_time": 2.92950080617481, + "invocations": 64 + }, + "children": [ + { + "entry": { + "name": "void vllm::rms_norm_kernel(c10::BFloat16*, c10::BFloat16 const*, c10::BFloat16 const*, float, int, int)", + "cuda_time_us": 4.511, + "pct_cuda_time": 0.0650564322366087, + "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": 198.6200000000001, + "pct_cuda_time": 2.8644443739382015, + "invocations": 63 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cuda_time_us": 1925.8909999999994, + "pct_cuda_time": 27.774683515095223, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cuda_time_us": 702.101, + "pct_cuda_time": 10.125512332022879, + "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": 702.101, + "pct_cuda_time": 10.125512332022879, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cuda_time_us": 121.31000000000004, + "pct_cuda_time": 1.7495002869924638, + "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": 121.31000000000004, + "pct_cuda_time": 1.7495002869924638, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cuda_time_us": 578.613, + "pct_cuda_time": 8.344601513128104, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cuda_time_us": 81.11700000000002, + "pct_cuda_time": 1.1698476199815984, + "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": 452.4080000000002, + "pct_cuda_time": 6.524506848880445, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cuda_time_us": 45.08800000000001, + "pct_cuda_time": 0.6502470442660638, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cuda_time_us": 523.8670000000001, + "pct_cuda_time": 7.5550693829517845, + "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": 523.8670000000001, + "pct_cuda_time": 7.5550693829517845, + "invocations": 32 + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cuda_time_us": 4312.453, + "pct_cuda_time": 62.19304064909331, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cuda_time_us": 2611.0359999999996, + "pct_cuda_time": 37.655660962391, + "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": 2611.0359999999996, + "pct_cuda_time": 37.655660962391, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cuda_time_us": 290.4629999999999, + "pct_cuda_time": 4.188979489413006, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cuda_time_us": 290.4629999999999, + "pct_cuda_time": 4.188979489413006, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cuda_time_us": 1410.954, + "pct_cuda_time": 20.348400197289294, + "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": 1410.954, + "pct_cuda_time": 20.348400197289294, + "invocations": 32 + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + 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0.010614394618963425, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cuda_time_us": 341.723, + "pct_cuda_time": 4.928237462467443, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cuda_time_us": 127.678, + "pct_cuda_time": 1.8413378752174077, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cuda_time_us": 5.374999999999999, + "pct_cuda_time": 0.07751680852843532, + "invocations": 7 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cuda_time_us": 6.976, + "pct_cuda_time": 0.10060600117104462, + "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": 9.728, + "pct_cuda_time": 0.14029460713760353, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::SoftMaxForwardEpilogue>(float*, float const*, int)", + "cuda_time_us": 34.943, + "pct_cuda_time": 0.5039385749598355, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::LogSoftMaxForwardEpilogue>(float*, float const*, int)", + "cuda_time_us": 28.223, + "pct_cuda_time": 0.40702453713451725, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cuda_time_us": 1.696, + "pct_cuda_time": 0.024459257165437457, + "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": 10.176, + "pct_cuda_time": 0.14675554299262472, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, long, 4> >(at::native::ReduceOp, unsigned int, long, 4>)", + "cuda_time_us": 27.936, + "pct_cuda_time": 0.4028855001023944, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cuda_time_us": 2.625, + "pct_cuda_time": 0.03785704602551493, + "invocations": 1 + }, + "children": [] + } + ] + } + ], + "model_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cpu_time_us": 80061.032, + "cuda_time_us": 6455.811, + "pct_cuda_time": 93.10397491772402, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cpu_time_us": 273.216, + "cuda_time_us": 11.2, + "pct_cuda_time": 0.16152339637553037, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 11.2, + "pct_cuda_time": 0.16152339637553037, + "trace": "index_select(bfloat16[128256, 4096], 0, int64[14]) <- embedding(bfloat16[128256, 4096], int64[14], -1, False, False)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 4042.301, + "cuda_time_us": 210.141, + "pct_cuda_time": 3.0305971462277075, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 267.716, + "cuda_time_us": 4.511, + "pct_cuda_time": 0.0650564322366087, + "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": 4.511, + "pct_cuda_time": 0.0650564322366087, + "trace": "_C::rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2903.653, + "cuda_time_us": 67.55199999999999, + "pct_cuda_time": 0.974216827853556, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 477.499, + "cuda_time_us": 27.552, + "pct_cuda_time": 0.3973475550838047, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 27.552, + "pct_cuda_time": 0.3973475550838047, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 887.343, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.054456459349464525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.054456459349464525, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 999.566, + "cuda_time_us": 19.776, + "pct_cuda_time": 0.285204168457365, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03553514720261668, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 15.84, + "pct_cuda_time": 0.22844023201682154, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.02122878923792685, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 280.364, + "cuda_time_us": 16.448, + "pct_cuda_time": 0.23720864496292174, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23720864496292174, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 126.856, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 608.327, + "cuda_time_us": 134.91, + "pct_cuda_time": 1.9456358397341789, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 223.492, + "cuda_time_us": 81.919, + "pct_cuda_time": 1.1814138489006314, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.919, + "pct_cuda_time": 1.1814138489006314, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 134.342, + "cuda_time_us": 9.152, + "pct_cuda_time": 0.1319876896097191, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.152, + "pct_cuda_time": 0.1319876896097191, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 166.815, + "cuda_time_us": 43.839, + "pct_cuda_time": 0.6322343012238282, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.839, + "pct_cuda_time": 0.6322343012238282, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2498.588, + "cuda_time_us": 201.308, + "pct_cuda_time": 2.903209989068327, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.605, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04475063383511346, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04475063383511346, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1774.951, + "cuda_time_us": 59.48800000000001, + "pct_cuda_time": 0.8579199824631742, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.752, + "cuda_time_us": 21.312, + "pct_cuda_time": 0.30735594853172354, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.312, + "pct_cuda_time": 0.30735594853172354, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 538.872, + "cuda_time_us": 3.968, + "pct_cuda_time": 0.05722543185875933, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.968, + "pct_cuda_time": 0.05722543185875933, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 738.648, + "cuda_time_us": 18.016000000000002, + "pct_cuda_time": 0.259821920455496, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.465, + "pct_cuda_time": 0.03554956893443592, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.271, + "pct_cuda_time": 0.20581253479242806, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01845981672863204, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 189.682, + "cuda_time_us": 16.192, + "pct_cuda_time": 0.23351668161719535, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23351668161719535, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.124, + "cuda_time_us": 3.231, + "pct_cuda_time": 0.04659661550797666, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.231, + "pct_cuda_time": 0.04659661550797666, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 465.662, + "cuda_time_us": 135.486, + "pct_cuda_time": 1.953942757262063, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 161.112, + "cuda_time_us": 81.631, + "pct_cuda_time": 1.1772603901366891, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.631, + "pct_cuda_time": 1.1772603901366891, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.625, + "cuda_time_us": 9.151, + "pct_cuda_time": 0.13197326787789987, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.151, + "pct_cuda_time": 0.13197326787789987, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.678, + "cuda_time_us": 44.704, + "pct_cuda_time": 0.6447090992474741, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.704, + "pct_cuda_time": 0.6447090992474741, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2447.671, + "cuda_time_us": 199.77100000000002, + "pct_cuda_time": 2.88104378726215, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.727, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04475063383511346, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04475063383511346, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1770.714, + "cuda_time_us": 59.391000000000005, + "pct_cuda_time": 0.8565210744767077, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.351, + "cuda_time_us": 21.408, + "pct_cuda_time": 0.3087404347863709, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.408, + "pct_cuda_time": 0.3087404347863709, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 530.03, + "cuda_time_us": 3.936, + "pct_cuda_time": 0.056763936440543526, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.056763936440543526, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 761.541, + "cuda_time_us": 17.887, + "pct_cuda_time": 0.25796151705081355, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036458138039048286, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.015, + "pct_cuda_time": 0.20212057144670162, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.344, + "pct_cuda_time": 0.019382807565063644, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 185.446, + "cuda_time_us": 16.16, + "pct_cuda_time": 0.23305518619897952, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23305518619897952, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.605, + "cuda_time_us": 3.071, + "pct_cuda_time": 0.04428913841689766, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04428913841689766, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 435.474, + "cuda_time_us": 134.20600000000002, + "pct_cuda_time": 1.9354829405334315, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 144.84, + "cuda_time_us": 81.215, + "pct_cuda_time": 1.1712609496998838, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.215, + "pct_cuda_time": 1.1712609496998838, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.366, + "cuda_time_us": 8.864, + "pct_cuda_time": 0.1278342308457769, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.864, + "pct_cuda_time": 0.1278342308457769, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.271, + "cuda_time_us": 44.127, + "pct_cuda_time": 0.6363877599877704, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.127, + "pct_cuda_time": 0.6363877599877704, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2306.303, + "cuda_time_us": 202.94, + "pct_cuda_time": 2.926746255397333, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.809, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.046611037239795906, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.046611037239795906, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1655.314, + "cuda_time_us": 60.22200000000001, + "pct_cuda_time": 0.8685055336184991, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.236, + "cuda_time_us": 21.823, + "pct_cuda_time": 0.3147254534913571, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.823, + "pct_cuda_time": 0.3147254534913571, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 496.987, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05307197309481713, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05307197309481713, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 699.364, + "cuda_time_us": 18.111, + "pct_cuda_time": 0.26119198497832413, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.037842624293695684, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.015, + "pct_cuda_time": 0.20212057144670162, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.02122878923792685, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 175.275, + "cuda_time_us": 16.608, + "pct_cuda_time": 0.23951612205400075, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.608, + "pct_cuda_time": 0.23951612205400075, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.502, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.044779477298751945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044779477298751945, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 429.488, + "cuda_time_us": 136.381, + "pct_cuda_time": 1.9668502072402863, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 142.587, + "cuda_time_us": 82.942, + "pct_cuda_time": 1.1961672805517176, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 82.942, + "pct_cuda_time": 1.1961672805517176, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 92.865, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.13152619419150327, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13152619419150327, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.332, + "cuda_time_us": 44.319, + "pct_cuda_time": 0.6391567324970653, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.319, + "pct_cuda_time": 0.6391567324970653, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2362.519, + "cuda_time_us": 200.667, + "pct_cuda_time": 2.893965658972192, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.87, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04614954182158011, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04614954182158011, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1673.401, + "cuda_time_us": 59.55, + "pct_cuda_time": 0.8588141298359673, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 142.392, + "cuda_time_us": 21.727, + "pct_cuda_time": 0.3133409672367097, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.3133409672367097, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 474.866, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05307197309481713, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05307197309481713, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 696.152, + "cuda_time_us": 18.015, + "pct_cuda_time": 0.25980749872367676, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03553514720261668, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.111, + "pct_cuda_time": 0.20350505770134905, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.020767293819711048, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 185.498, + "cuda_time_us": 16.128, + "pct_cuda_time": 0.23259369078076375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.128, + "pct_cuda_time": 0.23259369078076375, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.197, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04522655098514851, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04522655098514851, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 448.108, + "cuda_time_us": 134.781, + "pct_cuda_time": 1.9437754363294966, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 148.492, + "cuda_time_us": 81.631, + "pct_cuda_time": 1.1772603901366891, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.631, + "pct_cuda_time": 1.1772603901366891, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.885, + "cuda_time_us": 8.959, + "pct_cuda_time": 0.12920429536860506, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.959, + "pct_cuda_time": 0.12920429536860506, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.81, + "cuda_time_us": 44.191, + "pct_cuda_time": 0.6373107508242021, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.191, + "pct_cuda_time": 0.6373107508242021, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2653.794, + "cuda_time_us": 201.30900000000003, + "pct_cuda_time": 2.903224410800147, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.488, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.0447650555669327, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0447650555669327, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1948.746, + "cuda_time_us": 60.44800000000001, + "pct_cuda_time": 0.8717648450096482, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 140.86, + "cuda_time_us": 21.792, + "pct_cuda_time": 0.31427837980496054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.31427837980496054, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 516.286, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05491795476768032, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05491795476768032, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 932.684, + "cuda_time_us": 18.048000000000002, + "pct_cuda_time": 0.2602834158737118, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036458138039048286, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.24, + "pct_cuda_time": 0.20536546110603146, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01845981672863204, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 207.639, + "cuda_time_us": 16.8, + "pct_cuda_time": 0.24228509456329556, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.8, + "pct_cuda_time": 0.24228509456329556, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.133, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04522655098514851, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04522655098514851, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 444.715, + "cuda_time_us": 134.621, + "pct_cuda_time": 1.9414679592384174, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 145.145, + "cuda_time_us": 81.63, + "pct_cuda_time": 1.17724596840487, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.63, + "pct_cuda_time": 1.17724596840487, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.661, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.12968021251864012, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.12968021251864012, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.312, + "cuda_time_us": 43.999, + "pct_cuda_time": 0.6345417783149072, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.999, + "pct_cuda_time": 0.6345417783149072, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2298.199, + "cuda_time_us": 200.925, + "pct_cuda_time": 2.897686465781557, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.807, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1655.804, + "cuda_time_us": 59.934, + "pct_cuda_time": 0.8643520748545569, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.935, + "cuda_time_us": 21.503, + "pct_cuda_time": 0.31011049930919904, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.503, + "pct_cuda_time": 0.31011049930919904, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 497.915, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.054456459349464525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.054456459349464525, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 692.336, + "cuda_time_us": 17.696, + "pct_cuda_time": 0.255206966273338, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036458138039048286, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 13.856, + "pct_cuda_time": 0.19982751608744187, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.018921312146847842, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 172.06, + "cuda_time_us": 16.959, + "pct_cuda_time": 0.2445781499225553, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.959, + "pct_cuda_time": 0.2445781499225553, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.926, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 426.383, + "cuda_time_us": 134.655, + "pct_cuda_time": 1.9419582981202717, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 142.54, + "cuda_time_us": 82.047, + "pct_cuda_time": 1.1832598305734945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 82.047, + "pct_cuda_time": 1.1832598305734945, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.137, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.1301417079368559, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1301417079368559, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.136, + "cuda_time_us": 43.584, + "pct_cuda_time": 0.6285567596099211, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.584, + "pct_cuda_time": 0.6285567596099211, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2366.721, + "cuda_time_us": 200.637, + "pct_cuda_time": 2.8935330070176146, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.922, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04522655098514851, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04522655098514851, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1679.378, + "cuda_time_us": 59.711, + "pct_cuda_time": 0.8611360286588656, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.48, + "cuda_time_us": 21.76, + "pct_cuda_time": 0.31381688438674477, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.31381688438674477, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 494.478, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.054456459349464525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.054456459349464525, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 702.293, + "cuda_time_us": 17.791, + "pct_cuda_time": 0.2565770307961662, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.495, + "pct_cuda_time": 0.03598222088901324, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 13.824, + "pct_cuda_time": 0.19936602066922604, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.02122878923792685, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 195.051, + "cuda_time_us": 16.384, + "pct_cuda_time": 0.23628565412649014, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23628565412649014, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.945, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04522655098514851, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04522655098514851, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 459.408, + "cuda_time_us": 134.654, + "pct_cuda_time": 1.9419438763884522, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 154.618, + "cuda_time_us": 81.247, + "pct_cuda_time": 1.1717224451180996, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.247, + "pct_cuda_time": 1.1717224451180996, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.077, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.12875722168220852, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.12875722168220852, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.44, + "cuda_time_us": 44.479, + "pct_cuda_time": 0.6414642095881442, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.479, + "pct_cuda_time": 0.6414642095881442, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2315.515, + "cuda_time_us": 201.086, + "pct_cuda_time": 2.900008364604455, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.005, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1637.49, + "cuda_time_us": 59.742999999999995, + "pct_cuda_time": 0.8615975240770812, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 141.456, + "cuda_time_us": 21.375, + "pct_cuda_time": 0.30826451763633583, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.375, + "pct_cuda_time": 0.30826451763633583, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 502.174, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.05353346851303292, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05353346851303292, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 674.133, + "cuda_time_us": 18.496000000000002, + "pct_cuda_time": 0.26674435172873306, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03691963345726408, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.24, + "pct_cuda_time": 0.20536546110603146, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.696, + "pct_cuda_time": 0.024459257165437457, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 172.643, + "cuda_time_us": 16.16, + "pct_cuda_time": 0.23305518619897952, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23305518619897952, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.438, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.0447650555669327, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0447650555669327, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 445.984, + "cuda_time_us": 135.071, + "pct_cuda_time": 1.9479577385570772, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 147.2, + "cuda_time_us": 81.343, + "pct_cuda_time": 1.1731069313727471, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.343, + "pct_cuda_time": 1.1731069313727471, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.389, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.13152619419150327, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13152619419150327, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 135.521, + "cuda_time_us": 44.608, + "pct_cuda_time": 0.6433246129928266, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.608, + "pct_cuda_time": 0.6433246129928266, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2454.045, + "cuda_time_us": 201.69299999999998, + "pct_cuda_time": 2.908762355818736, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.162, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1757.497, + "cuda_time_us": 60.254999999999995, + "pct_cuda_time": 0.8689814507685341, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 215.501, + "cuda_time_us": 22.271, + "pct_cuda_time": 0.32118638934637833, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.271, + "pct_cuda_time": 0.32118638934637833, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 490.886, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.054456459349464525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.054456459349464525, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 704.126, + "cuda_time_us": 17.92, + "pct_cuda_time": 0.25843743420084864, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03507365178440088, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.208, + "pct_cuda_time": 0.2049039656878157, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01845981672863204, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 195.446, + "cuda_time_us": 16.288, + "pct_cuda_time": 0.23490116787184276, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23490116787184276, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 88.097, + "cuda_time_us": 3.263, + "pct_cuda_time": 0.04705811092619246, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.263, + "pct_cuda_time": 0.04705811092619246, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 462.251, + "cuda_time_us": 135.007, + "pct_cuda_time": 1.9470347477206453, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.134, + "cuda_time_us": 80.927, + "pct_cuda_time": 1.1671074909359418, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 80.927, + "pct_cuda_time": 1.1671074909359418, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.479, + "cuda_time_us": 9.248, + "pct_cuda_time": 0.1333721758643665, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1333721758643665, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.116, + "cuda_time_us": 44.832, + "pct_cuda_time": 0.6465550809203373, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.832, + "pct_cuda_time": 0.6465550809203373, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2350.75, + "cuda_time_us": 200.255, + "pct_cuda_time": 2.888023905462664, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.212, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.0447650555669327, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0447650555669327, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1686.356, + "cuda_time_us": 59.072, + "pct_cuda_time": 0.8519205420263689, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.828, + "cuda_time_us": 21.696, + "pct_cuda_time": 0.31289389355031316, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.696, + "pct_cuda_time": 0.31289389355031316, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 486.11, + "cuda_time_us": 3.745, + "pct_cuda_time": 0.05400938566306796, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.745, + "pct_cuda_time": 0.05400938566306796, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 719.6, + "cuda_time_us": 17.663, + "pct_cuda_time": 0.25473104912330297, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03691963345726408, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 13.791, + "pct_cuda_time": 0.198890103519191, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.018921312146847842, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 180.985, + "cuda_time_us": 15.968, + "pct_cuda_time": 0.23028621368968472, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.968, + "pct_cuda_time": 0.23028621368968472, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.403, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.044779477298751945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044779477298751945, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 432.0, + "cuda_time_us": 134.974, + "pct_cuda_time": 1.94655883057061, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 142.174, + "cuda_time_us": 80.991, + "pct_cuda_time": 1.1680304817723732, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 80.991, + "pct_cuda_time": 1.1680304817723732, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.581, + "cuda_time_us": 9.216, + "pct_cuda_time": 0.1329106804461507, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1329106804461507, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.157, + "cuda_time_us": 44.767, + "pct_cuda_time": 0.6456176683520864, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.767, + "pct_cuda_time": 0.6456176683520864, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2420.726, + "cuda_time_us": 201.21299999999997, + "pct_cuda_time": 2.9018399245454987, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.734, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1748.085, + "cuda_time_us": 60.510999999999996, + "pct_cuda_time": 0.8726734141142605, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.212, + "cuda_time_us": 22.24, + "pct_cuda_time": 0.3207393156599817, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.24, + "pct_cuda_time": 0.3207393156599817, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 462.775, + "cuda_time_us": 3.871, + "pct_cuda_time": 0.05582652387229268, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.871, + "pct_cuda_time": 0.05582652387229268, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 709.812, + "cuda_time_us": 18.112000000000002, + "pct_cuda_time": 0.2612064067101434, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036458138039048286, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 13.952, + "pct_cuda_time": 0.20121200234208925, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.632, + "pct_cuda_time": 0.02353626632900585, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 195.622, + "cuda_time_us": 16.288, + "pct_cuda_time": 0.23490116787184276, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23490116787184276, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.202, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04475063383511346, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04475063383511346, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 439.306, + "cuda_time_us": 134.43099999999998, + "pct_cuda_time": 1.9387278301927608, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 145.01, + "cuda_time_us": 81.759, + "pct_cuda_time": 1.1791063718095525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.759, + "pct_cuda_time": 1.1791063718095525, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.659, + "cuda_time_us": 8.961, + "pct_cuda_time": 0.12923313883224355, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.12923313883224355, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.699, + "cuda_time_us": 43.711, + "pct_cuda_time": 0.630388319550965, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.711, + "pct_cuda_time": 0.630388319550965, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2572.462, + "cuda_time_us": 201.341, + "pct_cuda_time": 2.903685906218363, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.918, + "cuda_time_us": 3.296, + "pct_cuda_time": 0.04753402807622751, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04753402807622751, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1895.383, + "cuda_time_us": 59.774, + "pct_cuda_time": 0.862044597763478, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.522, + "cuda_time_us": 21.791, + "pct_cuda_time": 0.3142639580731413, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.3142639580731413, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 532.273, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.052610477676601326, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.052610477676601326, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 873.367, + "cuda_time_us": 18.047, + "pct_cuda_time": 0.26026899414189253, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.431, + "pct_cuda_time": 0.03505923005258164, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.144, + "pct_cuda_time": 0.20398097485138408, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.02122878923792685, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 198.185, + "cuda_time_us": 16.288, + "pct_cuda_time": 0.23490116787184276, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23490116787184276, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.622, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 447.517, + "cuda_time_us": 135.103, + "pct_cuda_time": 1.9484192339752928, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 151.907, + "cuda_time_us": 82.175, + "pct_cuda_time": 1.1851058122463578, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 82.175, + "pct_cuda_time": 1.1851058122463578, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.682, + "cuda_time_us": 9.44, + "pct_cuda_time": 0.1361411483736613, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.44, + "pct_cuda_time": 0.1361411483736613, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 139.596, + "cuda_time_us": 43.488, + "pct_cuda_time": 0.6271722733552736, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.488, + "pct_cuda_time": 0.6271722733552736, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2298.171, + "cuda_time_us": 200.50799999999998, + "pct_cuda_time": 2.8916726036129323, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.354, + "cuda_time_us": 3.105, + "pct_cuda_time": 0.044779477298751945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044779477298751945, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1628.379, + "cuda_time_us": 60.222, + "pct_cuda_time": 0.8685055336184991, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.596, + "cuda_time_us": 21.696, + "pct_cuda_time": 0.31289389355031316, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.696, + "pct_cuda_time": 0.31289389355031316, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 488.188, + "cuda_time_us": 3.936, + "pct_cuda_time": 0.056763936440543526, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.056763936440543526, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 685.653, + "cuda_time_us": 18.078, + "pct_cuda_time": 0.2607160678282891, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.463, + "pct_cuda_time": 0.03552072547079744, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.304, + "pct_cuda_time": 0.20628845194246306, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.311, + "pct_cuda_time": 0.0189068904150286, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 171.551, + "cuda_time_us": 16.512, + "pct_cuda_time": 0.23813163579935334, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23813163579935334, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.948, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04475063383511346, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04475063383511346, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 448.38, + "cuda_time_us": 134.078, + "pct_cuda_time": 1.933636958860568, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.729, + "cuda_time_us": 81.407, + "pct_cuda_time": 1.1740299222091786, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.407, + "pct_cuda_time": 1.1740299222091786, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.527, + "cuda_time_us": 8.896, + "pct_cuda_time": 0.12829572626399272, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.12829572626399272, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.282, + "cuda_time_us": 43.775, + "pct_cuda_time": 0.6313113103873965, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.775, + "pct_cuda_time": 0.6313113103873965, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2333.564, + "cuda_time_us": 199.58300000000003, + "pct_cuda_time": 2.8783325016801324, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.359, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044303560148716906, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044303560148716906, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1660.684, + "cuda_time_us": 59.52, + "pct_cuda_time": 0.8583814778813901, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 177.59, + "cuda_time_us": 21.856, + "pct_cuda_time": 0.31520137064139214, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.31520137064139214, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 471.444, + "cuda_time_us": 3.936, + "pct_cuda_time": 0.056763936440543526, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.056763936440543526, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 681.435, + "cuda_time_us": 17.6, + "pct_cuda_time": 0.25382248001869057, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036458138039048286, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 13.792, + "pct_cuda_time": 0.19890452525101027, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01845981672863204, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 185.699, + "cuda_time_us": 16.128, + "pct_cuda_time": 0.23259369078076375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.128, + "pct_cuda_time": 0.23259369078076375, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.568, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.046611037239795906, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.046611037239795906, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 445.201, + "cuda_time_us": 133.75900000000001, + "pct_cuda_time": 1.9290364264102293, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 148.752, + "cuda_time_us": 81.311, + "pct_cuda_time": 1.1726454359545313, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.311, + "pct_cuda_time": 1.1726454359545313, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.209, + "cuda_time_us": 8.961, + "pct_cuda_time": 0.12923313883224355, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.12923313883224355, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 139.372, + "cuda_time_us": 43.487, + "pct_cuda_time": 0.6271578516234545, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.487, + "pct_cuda_time": 0.6271578516234545, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2338.234, + "cuda_time_us": 200.317, + "pct_cuda_time": 2.8889180528354568, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.154, + "cuda_time_us": 3.391, + "pct_cuda_time": 0.04890409259905566, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.391, + "pct_cuda_time": 0.04890409259905566, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1663.63, + "cuda_time_us": 59.52, + "pct_cuda_time": 0.8583814778813901, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.12, + "cuda_time_us": 21.504, + "pct_cuda_time": 0.3101249210410183, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.504, + "pct_cuda_time": 0.3101249210410183, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 511.677, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05491795476768032, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05491795476768032, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 690.986, + "cuda_time_us": 17.824, + "pct_cuda_time": 0.2570529479462012, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.035996642620832485, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 13.856, + "pct_cuda_time": 0.19982751608744187, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.02122878923792685, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 177.285, + "cuda_time_us": 16.384, + "pct_cuda_time": 0.23628565412649014, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23628565412649014, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.929, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 444.122, + "cuda_time_us": 134.238, + "pct_cuda_time": 1.935944435951647, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 150.252, + "cuda_time_us": 81.695, + "pct_cuda_time": 1.1781833809731208, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.695, + "pct_cuda_time": 1.1781833809731208, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.268, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.1306032033550717, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1306032033550717, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.395, + "cuda_time_us": 43.487, + "pct_cuda_time": 0.6271578516234545, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.487, + "pct_cuda_time": 0.6271578516234545, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2261.654, + "cuda_time_us": 201.82, + "pct_cuda_time": 2.91059391575978, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.182, + "cuda_time_us": 3.263, + "pct_cuda_time": 0.04705811092619246, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.263, + "pct_cuda_time": 0.04705811092619246, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1610.815, + "cuda_time_us": 59.519000000000005, + "pct_cuda_time": 0.8583670561495708, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.223, + "cuda_time_us": 21.44, + "pct_cuda_time": 0.3092019302045867, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.44, + "pct_cuda_time": 0.3092019302045867, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 483.272, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.053994963931248724, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.053994963931248724, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 676.083, + "cuda_time_us": 18.175, + "pct_cuda_time": 0.26211497581475574, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036458138039048286, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.047, + "pct_cuda_time": 0.20258206686491745, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.6, + "pct_cuda_time": 0.023074770910790056, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 172.447, + "cuda_time_us": 16.16, + "pct_cuda_time": 0.23305518619897952, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23305518619897952, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.593, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.0447650555669327, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0447650555669327, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 426.268, + "cuda_time_us": 135.934, + "pct_cuda_time": 1.9604036931170843, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 143.273, + "cuda_time_us": 82.462, + "pct_cuda_time": 1.1892448492784808, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 82.462, + "pct_cuda_time": 1.1892448492784808, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.365, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.1306032033550717, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1306032033550717, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 134.051, + "cuda_time_us": 44.416, + "pct_cuda_time": 0.6405556404835318, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.416, + "pct_cuda_time": 0.6405556404835318, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2370.697, + "cuda_time_us": 201.788, + "pct_cuda_time": 2.9101324203415646, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.758, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.045240972716967746, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.045240972716967746, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1690.314, + "cuda_time_us": 59.998000000000005, + "pct_cuda_time": 0.8652750656909884, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 131.237, + "cuda_time_us": 21.568, + "pct_cuda_time": 0.3110479118774499, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.568, + "pct_cuda_time": 0.3110479118774499, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 513.642, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05491795476768032, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05491795476768032, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 697.616, + "cuda_time_us": 18.399, + "pct_cuda_time": 0.2653454437422664, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.815, + "pct_cuda_time": 0.04059717507117125, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.208, + "pct_cuda_time": 0.2049039656878157, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.019844302983279445, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 198.892, + "cuda_time_us": 16.223, + "pct_cuda_time": 0.23396375530359187, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.223, + "pct_cuda_time": 0.23396375530359187, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 88.132, + "cuda_time_us": 3.135, + "pct_cuda_time": 0.04521212925332926, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04521212925332926, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 446.18, + "cuda_time_us": 135.518, + "pct_cuda_time": 1.954404252680279, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 146.256, + "cuda_time_us": 81.695, + "pct_cuda_time": 1.1781833809731208, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.695, + "pct_cuda_time": 1.1781833809731208, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.721, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.1324491850279349, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1324491850279349, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.912, + "cuda_time_us": 44.639, + "pct_cuda_time": 0.6437716866792234, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.639, + "pct_cuda_time": 0.6437716866792234, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2370.206, + "cuda_time_us": 201.37400000000002, + "pct_cuda_time": 2.904161823368398, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.718, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044303560148716906, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044303560148716906, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1648.87, + "cuda_time_us": 60.12700000000001, + "pct_cuda_time": 0.8671354690956711, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.568, + "cuda_time_us": 21.535, + "pct_cuda_time": 0.31057199472741487, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.535, + "pct_cuda_time": 0.31057199472741487, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 470.727, + "cuda_time_us": 3.744, + "pct_cuda_time": 0.053994963931248724, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.053994963931248724, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 687.802, + "cuda_time_us": 17.984, + "pct_cuda_time": 0.25936042503728024, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03738112887547989, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.112, + "pct_cuda_time": 0.20351947943316825, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01845981672863204, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 182.393, + "cuda_time_us": 16.864, + "pct_cuda_time": 0.2432080853997272, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.864, + "pct_cuda_time": 0.2432080853997272, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.1, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.045240972716967746, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.045240972716967746, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 494.232, + "cuda_time_us": 135.038, + "pct_cuda_time": 1.9474818214070422, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 148.96, + "cuda_time_us": 81.791, + "pct_cuda_time": 1.1795678672277683, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.791, + "pct_cuda_time": 1.1795678672277683, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.118, + "cuda_time_us": 9.44, + "pct_cuda_time": 0.1361411483736613, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.44, + "pct_cuda_time": 0.1361411483736613, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 194.702, + "cuda_time_us": 43.807, + "pct_cuda_time": 0.6317728058056125, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.807, + "pct_cuda_time": 0.6317728058056125, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2578.856, + "cuda_time_us": 201.46800000000002, + "pct_cuda_time": 2.9055174661594068, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.323, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1749.575, + "cuda_time_us": 60.35000000000001, + "pct_cuda_time": 0.8703515152913625, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 140.462, + "cuda_time_us": 21.76, + "pct_cuda_time": 0.31381688438674477, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.31381688438674477, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 483.706, + "cuda_time_us": 3.679, + "pct_cuda_time": 0.053057551362997876, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.679, + "pct_cuda_time": 0.053057551362997876, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 792.974, + "cuda_time_us": 17.919, + "pct_cuda_time": 0.2584230124690294, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03691963345726408, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 13.887, + "pct_cuda_time": 0.20027458977383844, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.02122878923792685, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 183.373, + "cuda_time_us": 16.992, + "pct_cuda_time": 0.2450540670725904, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.992, + "pct_cuda_time": 0.2450540670725904, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.225, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04522655098514851, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04522655098514851, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 605.708, + "cuda_time_us": 134.814, + "pct_cuda_time": 1.9442513534795314, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 142.712, + "cuda_time_us": 81.791, + "pct_cuda_time": 1.1795678672277683, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.791, + "pct_cuda_time": 1.1795678672277683, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.066, + "cuda_time_us": 9.344, + "pct_cuda_time": 0.13475666211901388, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.344, + "pct_cuda_time": 0.13475666211901388, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 312.428, + "cuda_time_us": 43.679, + "pct_cuda_time": 0.6299268241327493, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.679, + "pct_cuda_time": 0.6299268241327493, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2376.654, + "cuda_time_us": 200.413, + "pct_cuda_time": 2.8903025390901043, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.426, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1690.918, + "cuda_time_us": 59.551, + "pct_cuda_time": 0.8588285515677866, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 138.912, + "cuda_time_us": 21.728, + "pct_cuda_time": 0.31335538896852894, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.31335538896852894, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 466.217, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.052610477676601326, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.052610477676601326, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 738.732, + "cuda_time_us": 18.144000000000002, + "pct_cuda_time": 0.2616679021283592, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.035996642620832485, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.208, + "pct_cuda_time": 0.2049039656878157, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.020767293819711048, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 199.007, + "cuda_time_us": 16.031, + "pct_cuda_time": 0.23119478279429706, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.031, + "pct_cuda_time": 0.23119478279429706, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.721, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.0447650555669327, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0447650555669327, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 447.583, + "cuda_time_us": 134.59, + "pct_cuda_time": 1.9410208855520208, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 148.884, + "cuda_time_us": 81.375, + "pct_cuda_time": 1.1735684267909627, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.375, + "pct_cuda_time": 1.1735684267909627, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.19, + "cuda_time_us": 9.408, + "pct_cuda_time": 0.13567965295544548, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13567965295544548, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.143, + "cuda_time_us": 43.807, + "pct_cuda_time": 0.6317728058056125, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.807, + "pct_cuda_time": 0.6317728058056125, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2304.747, + "cuda_time_us": 201.56400000000002, + "pct_cuda_time": 2.9069019524140542, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.422, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044303560148716906, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044303560148716906, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1618.942, + "cuda_time_us": 60.415, + "pct_cuda_time": 0.8712889278596132, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 138.451, + "cuda_time_us": 22.335, + "pct_cuda_time": 0.32210938018280993, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.335, + "pct_cuda_time": 0.32210938018280993, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 480.911, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05491795476768032, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05491795476768032, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 675.323, + "cuda_time_us": 17.952, + "pct_cuda_time": 0.2588989296190644, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03553514720261668, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.208, + "pct_cuda_time": 0.2049039656878157, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01845981672863204, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 173.223, + "cuda_time_us": 16.32, + "pct_cuda_time": 0.23536266329005853, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23536266329005853, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.563, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.046611037239795906, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.046611037239795906, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 458.383, + "cuda_time_us": 134.84500000000003, + "pct_cuda_time": 1.9446984271659282, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 148.039, + "cuda_time_us": 81.95, + "pct_cuda_time": 1.181860922587028, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.95, + "pct_cuda_time": 1.181860922587028, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.668, + "cuda_time_us": 8.832, + "pct_cuda_time": 0.1273727354275611, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1273727354275611, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 137.488, + "cuda_time_us": 44.063, + "pct_cuda_time": 0.6354647691513389, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.063, + "pct_cuda_time": 0.6354647691513389, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2312.394, + "cuda_time_us": 201.183, + "pct_cuda_time": 2.901407272590922, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.464, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1646.226, + "cuda_time_us": 60.25600000000001, + "pct_cuda_time": 0.8689958725003536, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.001, + "cuda_time_us": 21.792, + "pct_cuda_time": 0.31427837980496054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.31427837980496054, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 480.635, + "cuda_time_us": 3.968, + "pct_cuda_time": 0.05722543185875933, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.968, + "pct_cuda_time": 0.05722543185875933, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 695.016, + "cuda_time_us": 17.92, + "pct_cuda_time": 0.25843743420084864, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.72, + "pct_cuda_time": 0.039227110548343096, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 13.92, + "pct_cuda_time": 0.20075050692387347, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01845981672863204, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 195.761, + "cuda_time_us": 16.576, + "pct_cuda_time": 0.23905462663578497, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23905462663578497, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.018, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.0447650555669327, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0447650555669327, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 439.002, + "cuda_time_us": 134.655, + "pct_cuda_time": 1.9419582981202717, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 146.244, + "cuda_time_us": 81.631, + "pct_cuda_time": 1.1772603901366891, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.631, + "pct_cuda_time": 1.1772603901366891, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.337, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.12875722168220852, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.12875722168220852, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.801, + "cuda_time_us": 44.096, + "pct_cuda_time": 0.6359406863013738, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.096, + "pct_cuda_time": 0.6359406863013738, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2419.812, + "cuda_time_us": 200.894, + "pct_cuda_time": 2.8972393920951607, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.681, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1752.114, + "cuda_time_us": 60.223, + "pct_cuda_time": 0.8685199553503183, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 142.741, + "cuda_time_us": 21.92, + "pct_cuda_time": 0.31612436147782375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.31612436147782375, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 525.224, + "cuda_time_us": 3.775, + "pct_cuda_time": 0.05444203761764528, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05444203761764528, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 733.396, + "cuda_time_us": 18.176000000000002, + "pct_cuda_time": 0.262129397546575, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.816, + "pct_cuda_time": 0.04061159680299049, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 13.856, + "pct_cuda_time": 0.19982751608744187, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.02169028465614265, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 200.779, + "cuda_time_us": 16.352, + "pct_cuda_time": 0.23582415870827436, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23582415870827436, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.408, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.0447650555669327, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0447650555669327, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 433.368, + "cuda_time_us": 134.399, + "pct_cuda_time": 1.9382663347745452, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 146.514, + "cuda_time_us": 81.567, + "pct_cuda_time": 1.1763373993002575, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.567, + "pct_cuda_time": 1.1763373993002575, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.146, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.12968021251864012, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.12968021251864012, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 139.122, + "cuda_time_us": 43.84, + "pct_cuda_time": 0.6322487229556475, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.84, + "pct_cuda_time": 0.6322487229556475, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2283.505, + "cuda_time_us": 200.63700000000003, + "pct_cuda_time": 2.893533007017615, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.194, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04614954182158011, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04614954182158011, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1630.425, + "cuda_time_us": 59.968, + "pct_cuda_time": 0.8648424137364111, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.396, + "cuda_time_us": 21.76, + "pct_cuda_time": 0.31381688438674477, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.31381688438674477, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 478.952, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05307197309481713, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05307197309481713, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 679.245, + "cuda_time_us": 18.496000000000002, + "pct_cuda_time": 0.26674435172873306, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03553514720261668, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.336, + "pct_cuda_time": 0.20674994736067887, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.696, + "pct_cuda_time": 0.024459257165437457, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 182.099, + "cuda_time_us": 16.032, + "pct_cuda_time": 0.23120920452611632, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.032, + "pct_cuda_time": 0.23120920452611632, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.58, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044303560148716906, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044303560148716906, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 431.433, + "cuda_time_us": 134.39700000000002, + "pct_cuda_time": 1.9382374913109068, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 145.208, + "cuda_time_us": 81.79, + "pct_cuda_time": 1.179553445495949, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.79, + "pct_cuda_time": 1.179553445495949, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.066, + "cuda_time_us": 8.864, + "pct_cuda_time": 0.1278342308457769, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.864, + "pct_cuda_time": 0.1278342308457769, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.303, + "cuda_time_us": 43.743, + "pct_cuda_time": 0.6308498149691808, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.743, + "pct_cuda_time": 0.6308498149691808, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2334.514, + "cuda_time_us": 201.75900000000001, + "pct_cuda_time": 2.9097141901188066, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.778, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04522655098514851, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04522655098514851, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1681.688, + "cuda_time_us": 59.713, + "pct_cuda_time": 0.8611648721225041, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.878, + "cuda_time_us": 21.664, + "pct_cuda_time": 0.31243239813209733, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.664, + "pct_cuda_time": 0.31243239813209733, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 508.895, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05537945018589612, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05537945018589612, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 708.925, + "cuda_time_us": 18.081, + "pct_cuda_time": 0.2607593330237469, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03738112887547989, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.176, + "pct_cuda_time": 0.20444247026959986, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.018935733878667087, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 174.763, + "cuda_time_us": 16.128, + "pct_cuda_time": 0.23259369078076375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.128, + "pct_cuda_time": 0.23259369078076375, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.562, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04522655098514851, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04522655098514851, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 432.061, + "cuda_time_us": 135.774, + "pct_cuda_time": 1.9580962160260056, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 148.327, + "cuda_time_us": 82.143, + "pct_cuda_time": 1.184644316828142, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 82.143, + "pct_cuda_time": 1.184644316828142, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.909, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.1306032033550717, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1306032033550717, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.29, + "cuda_time_us": 44.575, + "pct_cuda_time": 0.6428486958427917, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.575, + "pct_cuda_time": 0.6428486958427917, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2246.045, + "cuda_time_us": 200.926, + "pct_cuda_time": 2.897700887513376, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.799, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044303560148716906, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044303560148716906, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1602.955, + "cuda_time_us": 59.743, + "pct_cuda_time": 0.8615975240770815, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.349, + "cuda_time_us": 21.759, + "pct_cuda_time": 0.3138024626549255, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.759, + "pct_cuda_time": 0.3138024626549255, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 477.18, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.054456459349464525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.054456459349464525, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 669.593, + "cuda_time_us": 18.144000000000002, + "pct_cuda_time": 0.2616679021283592, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036458138039048286, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.336, + "pct_cuda_time": 0.20674994736067887, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01845981672863204, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 169.416, + "cuda_time_us": 16.064, + "pct_cuda_time": 0.23167069994433215, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.064, + "pct_cuda_time": 0.23167069994433215, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.453, + "cuda_time_us": 3.265, + "pct_cuda_time": 0.04708695438983095, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.265, + "pct_cuda_time": 0.04708695438983095, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 427.008, + "cuda_time_us": 134.846, + "pct_cuda_time": 1.944712848897747, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 147.472, + "cuda_time_us": 81.407, + "pct_cuda_time": 1.1740299222091786, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.407, + "pct_cuda_time": 1.1740299222091786, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 92.25, + "cuda_time_us": 9.152, + "pct_cuda_time": 0.1319876896097191, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.152, + "pct_cuda_time": 0.1319876896097191, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 134.884, + "cuda_time_us": 44.287, + "pct_cuda_time": 0.6386952370788495, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.287, + "pct_cuda_time": 0.6386952370788495, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2694.602, + "cuda_time_us": 200.735, + "pct_cuda_time": 2.894946336735901, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.148, + "cuda_time_us": 3.264, + "pct_cuda_time": 0.0470725326580117, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0470725326580117, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2001.478, + "cuda_time_us": 60.480000000000004, + "pct_cuda_time": 0.8722263404278641, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 133.825, + "cuda_time_us": 22.048, + "pct_cuda_time": 0.3179703431506869, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.048, + "pct_cuda_time": 0.3179703431506869, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 568.298, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05307197309481713, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05307197309481713, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 940.098, + "cuda_time_us": 18.016000000000002, + "pct_cuda_time": 0.259821920455496, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036458138039048286, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.016, + "pct_cuda_time": 0.20213499317852085, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.02122878923792685, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 215.498, + "cuda_time_us": 16.736, + "pct_cuda_time": 0.24136210372686395, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.736, + "pct_cuda_time": 0.24136210372686395, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 94.666, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04614954182158011, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04614954182158011, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 453.545, + "cuda_time_us": 133.791, + "pct_cuda_time": 1.929497921828445, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 151.339, + "cuda_time_us": 80.736, + "pct_cuda_time": 1.164352940158466, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 80.736, + "pct_cuda_time": 1.164352940158466, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.046, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.12875722168220852, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.12875722168220852, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 143.589, + "cuda_time_us": 44.127, + "pct_cuda_time": 0.6363877599877704, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.127, + "pct_cuda_time": 0.6363877599877704, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2334.38, + "cuda_time_us": 200.798, + "pct_cuda_time": 2.895854905840513, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.466, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1659.81, + "cuda_time_us": 60.033, + "pct_cuda_time": 0.8657798263046621, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 143.312, + "cuda_time_us": 21.632, + "pct_cuda_time": 0.31197090271388156, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.632, + "pct_cuda_time": 0.31197090271388156, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 487.107, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05307197309481713, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05307197309481713, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 683.699, + "cuda_time_us": 18.305, + "pct_cuda_time": 0.2639898009512574, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03507365178440088, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.401, + "pct_cuda_time": 0.20768735992892973, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.02122878923792685, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 192.468, + "cuda_time_us": 16.416, + "pct_cuda_time": 0.23674714954470596, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23674714954470596, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.363, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.0447650555669327, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0447650555669327, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 440.908, + "cuda_time_us": 134.493, + "pct_cuda_time": 1.9396219775655539, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 146.958, + "cuda_time_us": 81.054, + "pct_cuda_time": 1.1689390508769857, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.054, + "pct_cuda_time": 1.1689390508769857, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.889, + "cuda_time_us": 9.152, + "pct_cuda_time": 0.1319876896097191, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.152, + "pct_cuda_time": 0.1319876896097191, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 137.87, + "cuda_time_us": 44.287, + "pct_cuda_time": 0.6386952370788495, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.287, + "pct_cuda_time": 0.6386952370788495, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2378.262, + "cuda_time_us": 201.02100000000002, + "pct_cuda_time": 2.899070952036205, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.218, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.0438420647305011, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0438420647305011, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1711.637, + "cuda_time_us": 60.637, + "pct_cuda_time": 0.8744905523234852, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 138.069, + "cuda_time_us": 22.303, + "pct_cuda_time": 0.3216478847645941, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.303, + "pct_cuda_time": 0.3216478847645941, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 480.634, + "cuda_time_us": 4.192, + "pct_cuda_time": 0.060455899786269945, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 4.192, + "pct_cuda_time": 0.060455899786269945, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 691.866, + "cuda_time_us": 17.919, + "pct_cuda_time": 0.2584230124690294, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03553514720261668, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.175, + "pct_cuda_time": 0.20442804853778063, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.01845981672863204, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 251.678, + "cuda_time_us": 16.223, + "pct_cuda_time": 0.23396375530359187, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.223, + "pct_cuda_time": 0.23396375530359187, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.291, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 430.581, + "cuda_time_us": 134.17600000000002, + "pct_cuda_time": 1.935050288578854, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 143.875, + "cuda_time_us": 80.768, + "pct_cuda_time": 1.1648144355766819, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 80.768, + "pct_cuda_time": 1.1648144355766819, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.945, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.12921871710042432, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.12921871710042432, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.487, + "cuda_time_us": 44.448, + "pct_cuda_time": 0.6410171359017477, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.448, + "pct_cuda_time": 0.6410171359017477, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2399.662, + "cuda_time_us": 201.022, + "pct_cuda_time": 2.8990853737680236, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.766, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.044303560148716906, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.044303560148716906, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1734.389, + "cuda_time_us": 59.839, + "pct_cuda_time": 0.8629820103317287, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.889, + "cuda_time_us": 21.952, + "pct_cuda_time": 0.3165858568960395, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.952, + "pct_cuda_time": 0.3165858568960395, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 505.506, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.054456459349464525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.054456459349464525, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 755.85, + "cuda_time_us": 18.048000000000002, + "pct_cuda_time": 0.2602834158737118, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036458138039048286, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 14.208, + "pct_cuda_time": 0.2049039656878157, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.018921312146847842, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 192.838, + "cuda_time_us": 16.063, + "pct_cuda_time": 0.2316562782125129, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.063, + "pct_cuda_time": 0.2316562782125129, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.328, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04614954182158011, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.04614954182158011, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 444.075, + "cuda_time_us": 134.911, + "pct_cuda_time": 1.945650261465998, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 151.17, + "cuda_time_us": 81.727, + "pct_cuda_time": 1.1786448763913366, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.727, + "pct_cuda_time": 1.1786448763913366, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.614, + "cuda_time_us": 9.28, + "pct_cuda_time": 0.1338336712825823, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1338336712825823, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 141.556, + "cuda_time_us": 43.904, + "pct_cuda_time": 0.633171713792079, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.904, + "pct_cuda_time": 0.633171713792079, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2281.576, + "cuda_time_us": 200.37900000000002, + "pct_cuda_time": 2.8898122002082505, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.363, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1641.366, + "cuda_time_us": 60.126000000000005, + "pct_cuda_time": 0.8671210473638518, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 138.053, + "cuda_time_us": 21.599, + "pct_cuda_time": 0.31149498556384647, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.599, + "pct_cuda_time": 0.31149498556384647, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[14, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 491.566, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05307197309481713, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05307197309481713, + "trace": "_C::rotary_embedding(int64[14], bfloat16[14, 4096], bfloat16[14, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 687.849, + "cuda_time_us": 17.855, + "pct_cuda_time": 0.2575000216325978, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.035996642620832485, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[14], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 13.855, + "pct_cuda_time": 0.1998130943556226, + "trace": "_vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.02169028465614265, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[14, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[14, 1, 32, 128], None, None, None, None, int32[14], None, None, int32[14, 17], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[14, 32, 128], bfloat16[14, 8, 128], bfloat16[14, 8, 128], bfloat16[14, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 172.486, + "cuda_time_us": 16.992, + "pct_cuda_time": 0.2450540670725904, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.992, + "pct_cuda_time": 0.2450540670725904, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[14, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.543, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.0456880464033643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0456880464033643, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 422.648, + "cuda_time_us": 133.917, + "pct_cuda_time": 1.9313150600376698, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 139.165, + "cuda_time_us": 81.279, + "pct_cuda_time": 1.1721839405363155, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.279, + "pct_cuda_time": 1.1721839405363155, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[14, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 92.65, + "cuda_time_us": 8.799, + "pct_cuda_time": 0.12689681827752602, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.799, + "pct_cuda_time": 0.12689681827752602, + "trace": "_C::silu_and_mul(bfloat16[14, 14336], bfloat16[14, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 137.372, + "cuda_time_us": 43.839, + "pct_cuda_time": 0.6322343012238282, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.839, + "pct_cuda_time": 0.6322343012238282, + "trace": "mm(bfloat16[14, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[14, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[14, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.73, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04522655098514851, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04522655098514851, + "trace": "_C::fused_add_rms_norm(bfloat16[14, 4096], bfloat16[14, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 503.87, + "cuda_time_us": 350.491, + "pct_cuda_time": 5.054687207058572, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 8.032, + "pct_cuda_time": 0.11583534997216607, + "trace": "index_select(bfloat16[14, 4096], 0, int64[14])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.010614394618963425, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[14, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 341.723, + "pct_cuda_time": 4.928237462467443, + "trace": "mm(bfloat16[14, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[14, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[14, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 3942.61, + "cuda_time_us": 127.678, + "pct_cuda_time": 1.8413378752174077, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.010614394618963425, + "trace": "copy_(bfloat16[14], bfloat16[14], True) <- _to_copy(bfloat16[14], 15, 0, None, None, True, None) <- to(bfloat16[14], 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": 0.010614394618963425, + "trace": "copy_(bfloat16[14], bfloat16[14], True) <- _to_copy(bfloat16[14], 15, 0, None, None, True, None) <- to(bfloat16[14], 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": 0.011061468305359982, + "trace": "copy_(int32[14], int32[14], True) <- _to_copy(int32[14], 3, 0, None, None, True, None) <- to(int32[14], 3, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.011075890037179226, + "trace": "copy_(bfloat16[14], bfloat16[14], True) <- _to_copy(bfloat16[14], 15, 0, None, None, True, None) <- to(bfloat16[14], 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": 0.011537385455395028, + "trace": "copy_(bfloat16[14], bfloat16[14], True) <- _to_copy(bfloat16[14], 15, 0, None, None, True, None) <- to(bfloat16[14], 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": 0.011537385455395028, + "trace": "copy_(bfloat16[14], bfloat16[14], True) <- _to_copy(bfloat16[14], 15, 0, None, None, True, None) <- to(bfloat16[14], 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": 0.011075890037179226, + "trace": "copy_(bfloat16[14], bfloat16[14], True) <- _to_copy(bfloat16[14], 15, 0, None, None, True, None) <- to(bfloat16[14], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 6.976, + "pct_cuda_time": 0.10060600117104462, + "trace": "copy_(float32[14, 128256], bfloat16[14, 128256], False) <- _to_copy(bfloat16[14, 128256], 6, None, None, None, False, None) <- to(bfloat16[14, 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": 9.728, + "pct_cuda_time": 0.14029460713760353, + "trace": "div_(float32[14, 128256], bfloat16[14, 1])" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::SoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.943, + "pct_cuda_time": 0.5039385749598355, + "trace": "_softmax(float32[14, 128256], -1, False) <- softmax(float32[14, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::LogSoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 28.223, + "pct_cuda_time": 0.40702453713451725, + "trace": "_log_softmax(float32[14, 128256], -1, False) <- log_softmax(float32[14, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 1.696, + "pct_cuda_time": 0.024459257165437457, + "trace": "copy_(int64[14], int32[14], False) <- _to_copy(int32[14], 4, None, None, None, False, None) <- to(int32[14], 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": 10.176, + "pct_cuda_time": 0.14675554299262472, + "trace": "index(float32[14, 128256], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, long, 4> >(at::native::ReduceOp, unsigned int, long, 4>)", + "cpu_time_us": 0, + "cuda_time_us": 27.936, + "pct_cuda_time": 0.4028855001023944, + "trace": "argmax(float32[14, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 2.625, + "pct_cuda_time": 0.03785704602551493, + "trace": "copy_(int64[14], int64[14], False) <- _to_copy(int64[14], 4, 0, None, None, False, None) <- to(int64[14], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + } +} \ No newline at end of file