diff --git "a/H100_llama8b_pp1_tp1/profiling_bs12_pl128.json" "b/H100_llama8b_pp1_tp1/profiling_bs12_pl128.json" new file mode 100644--- /dev/null +++ "b/H100_llama8b_pp1_tp1/profiling_bs12_pl128.json" @@ -0,0 +1,18219 @@ +{ + "context": { + "python_version": "3.12.9 | packaged by Anaconda, Inc. | (main, Feb 6 2025, 18:56:27) [GCC 11.2.0]", + "torch_version": "2.5.1+cu124", + "engine_args": { + "model": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", + "served_model_name": null, + "tokenizer": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", + "task": "auto", + "skip_tokenizer_init": false, + "tokenizer_mode": "auto", + "trust_remote_code": false, + "allowed_local_media_path": null, + "download_dir": null, + "load_format": "dummy", + "config_format": "auto", + "dtype": "auto", + "kv_cache_dtype": "auto", + "seed": 0, + "max_model_len": null, + "distributed_executor_backend": null, + "pipeline_parallel_size": 1, + "tensor_parallel_size": 1, + "max_parallel_loading_workers": null, + "block_size": null, + "enable_prefix_caching": false, + "disable_sliding_window": false, + "use_v2_block_manager": true, + "swap_space": 4, + "cpu_offload_gb": 0, + "gpu_memory_utilization": 0.9, + "max_num_batched_tokens": 8000, + "max_num_partial_prefills": 1, + "max_long_partial_prefills": 1, + "long_prefill_token_threshold": 0, + "max_num_seqs": 256, + "max_logprobs": 20, + "disable_log_stats": false, + "revision": null, + "code_revision": null, + "rope_scaling": null, + "rope_theta": null, + "hf_overrides": null, + "tokenizer_revision": null, + "quantization": null, + "enforce_eager": true, + "max_seq_len_to_capture": 8192, + "disable_custom_all_reduce": false, + "tokenizer_pool_size": 0, + "tokenizer_pool_type": "ray", + "tokenizer_pool_extra_config": null, + "limit_mm_per_prompt": null, + "mm_processor_kwargs": null, + "disable_mm_preprocessor_cache": false, + "enable_lora": false, + "enable_lora_bias": false, + "max_loras": 1, + "max_lora_rank": 16, + "enable_prompt_adapter": false, + "max_prompt_adapters": 1, + "max_prompt_adapter_token": 0, + "fully_sharded_loras": false, + "lora_extra_vocab_size": 256, + "long_lora_scaling_factors": null, + "lora_dtype": "auto", + "max_cpu_loras": null, + "device": "auto", + "num_scheduler_steps": 1, + "multi_step_stream_outputs": true, + "ray_workers_use_nsight": false, + "num_gpu_blocks_override": null, + "num_lookahead_slots": 0, + "model_loader_extra_config": null, + "ignore_patterns": [], + "preemption_mode": null, + "scheduler_delay_factor": 0.0, + "enable_chunked_prefill": null, + "guided_decoding_backend": "xgrammar", + "logits_processor_pattern": null, + "speculative_model": null, + "speculative_model_quantization": null, + "speculative_draft_tensor_parallel_size": null, + "num_speculative_tokens": null, + "speculative_disable_mqa_scorer": false, + "speculative_max_model_len": null, + "speculative_disable_by_batch_size": null, + "ngram_prompt_lookup_max": null, + "ngram_prompt_lookup_min": null, + "spec_decoding_acceptance_method": "rejection_sampler", + "typical_acceptance_sampler_posterior_threshold": null, + "typical_acceptance_sampler_posterior_alpha": null, + "qlora_adapter_name_or_path": null, + "disable_logprobs_during_spec_decoding": null, + "otlp_traces_endpoint": null, + "collect_detailed_traces": null, + "disable_async_output_proc": false, + "scheduling_policy": "fcfs", + "scheduler_cls": "vllm.core.scheduler.Scheduler", + "override_neuron_config": null, + "override_pooler_config": null, + "compilation_config": null, + "worker_cls": "auto", + "kv_transfer_config": null, + "generation_config": null, + "override_generation_config": null, + "enable_sleep_mode": false, + "model_impl": "auto", + "calculate_kv_scales": false, + "additional_config": null + }, + "prompt_len": 0, + "batch_size": 12, + "num_steps": 2, + "complete_num_requests_per_step": null, + "save_chrome_traces_folder": null + }, + "prefill": { + "metadata": { + "num_running_seqs": null + }, + "summary_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cuda_time_us": 33834.56799999999, + "pct_cuda_time": 98.56911706084959, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cuda_time_us": 43.999, + "pct_cuda_time": 0.12818081736880232, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", + "cuda_time_us": 43.999, + "pct_cuda_time": 0.12818081736880232, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cuda_time_us": 33776.009999999995, + "pct_cuda_time": 98.39852199497352, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cuda_time_us": 965.1080000000003, + "pct_cuda_time": 2.8116169069562966, + "invocations": 64 + }, + "children": [ + { + "entry": { + "name": "void vllm::rms_norm_kernel(c10::BFloat16*, c10::BFloat16 const*, c10::BFloat16 const*, float, int, int)", + "cuda_time_us": 19.615, + "pct_cuda_time": 0.05714372446394366, + "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": 945.4930000000003, + "pct_cuda_time": 2.7544731824923527, + "invocations": 63 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cuda_time_us": 7646.6230000000005, + "pct_cuda_time": 22.276651429602566, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cuda_time_us": 3433.4869999999996, + "pct_cuda_time": 10.002663017003952, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cuda_time_us": 23.90200000000001, + "pct_cuda_time": 0.06963289840107989, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cuda_time_us": 3409.5849999999996, + "pct_cuda_time": 9.933030118602872, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cuda_time_us": 623.357, + "pct_cuda_time": 1.8160051313112686, + "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": 623.357, + "pct_cuda_time": 1.8160051313112686, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cuda_time_us": 964.47, + "pct_cuda_time": 2.8097582428620824, + "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": 244.028, + "pct_cuda_time": 0.7109186231703922, + "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, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::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": 675.4809999999999, + "pct_cuda_time": 1.9678562398485404, + "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": 44.961000000000006, + "pct_cuda_time": 0.1309833798431492, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cuda_time_us": 2625.3090000000007, + "pct_cuda_time": 7.648225038425262, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cuda_time_us": 24.894000000000005, + "pct_cuda_time": 0.07252285887358725, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cuda_time_us": 2600.4150000000004, + "pct_cuda_time": 7.575702179551675, + "invocations": 32 + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cuda_time_us": 25164.27899999999, + "pct_cuda_time": 73.31025365841465, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cuda_time_us": 15461.015999999998, + "pct_cuda_time": 45.04206159758472, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cuda_time_us": 23.681000000000008, + "pct_cuda_time": 0.06898906648129749, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cuda_time_us": 15437.334999999997, + "pct_cuda_time": 44.97307253110342, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cuda_time_us": 2147.4289999999996, + "pct_cuda_time": 6.256033193060517, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cuda_time_us": 2147.4289999999996, + "pct_cuda_time": 6.256033193060517, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cuda_time_us": 7555.834, + "pct_cuda_time": 22.012158867769426, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cuda_time_us": 25.951000000000015, + "pct_cuda_time": 0.07560218167544241, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cuda_time_us": 7529.882999999999, + "pct_cuda_time": 21.93655668609398, + "invocations": 32 + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cuda_time_us": 14.559, + "pct_cuda_time": 0.04241424850729318, + "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": 14.559, + "pct_cuda_time": 0.04241424850729318, + "invocations": 1 + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cuda_time_us": 363.9, + "pct_cuda_time": 1.0601377176869282, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cuda_time_us": 7.233, + "pct_cuda_time": 0.021071657356497808, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "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": 355.931, + "pct_cuda_time": 1.0369218961088926, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cuda_time_us": 127.26100000000001, + "pct_cuda_time": 0.37074522146346856, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cuda_time_us": 5.3759999999999994, + "pct_cuda_time": 0.015661721270362534, + "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": 7.008, + "pct_cuda_time": 0.02041617237029402, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::elementwise_kernel<128, 4, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1})", + "cuda_time_us": 8.544, + "pct_cuda_time": 0.02489094987611189, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::SoftMaxForwardEpilogue>(float*, float const*, int)", + "cuda_time_us": 35.743, + "pct_cuda_time": 0.10412888827503128, + "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": 28.831, + "pct_cuda_time": 0.08399238949885086, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cuda_time_us": 2.048, + "pct_cuda_time": 0.005966370007757156, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::index_elementwise_kernel<128, 4, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1}>(long, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1})", + "cuda_time_us": 9.184, + "pct_cuda_time": 0.026755440503535993, + "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": 27.744, + "pct_cuda_time": 0.08082566869883523, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cuda_time_us": 2.783, + "pct_cuda_time": 0.008107620962689535, + "invocations": 1 + }, + "children": [] + } + ] + } + ], + "model_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cpu_time_us": 83684.435, + "cuda_time_us": 33834.56799999999, + "pct_cuda_time": 98.56911706084959, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cpu_time_us": 328.111, + "cuda_time_us": 43.999, + "pct_cuda_time": 0.12818081736880232, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectLargeIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 43.999, + "pct_cuda_time": 0.12818081736880232, + "trace": "index_select(bfloat16[128256, 4096], 0, int64[1536]) <- embedding(bfloat16[128256, 4096], int64[1536], -1, False, False)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 4162.671, + "cuda_time_us": 1065.1370000000002, + "pct_cuda_time": 3.103028052222869, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 230.052, + "cuda_time_us": 19.615, + "pct_cuda_time": 0.05714372446394366, + "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": 19.615, + "pct_cuda_time": 0.05714372446394366, + "trace": "_C::rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 3120.042, + "cuda_time_us": 244.861, + "pct_cuda_time": 0.713345374252649, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 436.677, + "cuda_time_us": 111.742, + "pct_cuda_time": 0.3255342370150392, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 111.006, + "pct_cuda_time": 0.3233900727935014, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 1029.931, + "cuda_time_us": 19.52, + "pct_cuda_time": 0.0568669641364354, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.52, + "pct_cuda_time": 0.0568669641364354, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1074.83, + "cuda_time_us": 30.08, + "pct_cuda_time": 0.08763105948893322, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.616, + "pct_cuda_time": 0.022187438466346923, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.151, + "pct_cuda_time": 0.06161850196976153, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0038251190528247785, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 333.033, + "cuda_time_us": 83.519, + "pct_cuda_time": 0.24331311361224117, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 82.783, + "pct_cuda_time": 0.24116894939070344, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 118.836, + "cuda_time_us": 15.519, + "pct_cuda_time": 0.04521098444842935, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.519, + "pct_cuda_time": 0.04521098444842935, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 574.447, + "cuda_time_us": 785.142, + "pct_cuda_time": 2.2873279690578467, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 191.43, + "cuda_time_us": 482.554, + "pct_cuda_time": 1.4058084534781479, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 481.818, + "pct_cuda_time": 1.40366428925661, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 133.913, + "cuda_time_us": 67.455, + "pct_cuda_time": 0.19651439886389596, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.455, + "pct_cuda_time": 0.19651439886389596, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 177.701, + "cuda_time_us": 235.13299999999998, + "pct_cuda_time": 0.6850051167158023, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.832, + "pct_cuda_time": 0.0024238378156513445, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 234.301, + "pct_cuda_time": 0.6825812789001511, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2626.917, + "cuda_time_us": 1048.625, + "pct_cuda_time": 3.0549241940353262, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.468, + "cuda_time_us": 14.56, + "pct_cuda_time": 0.04241716177389853, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.56, + "pct_cuda_time": 0.04241716177389853, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1913.217, + "cuda_time_us": 236.92300000000003, + "pct_cuda_time": 0.6902198639393794, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 162.606, + "cuda_time_us": 107.006, + "pct_cuda_time": 0.3117370063721007, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.27, + "pct_cuda_time": 0.309592842150563, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 566.641, + "cuda_time_us": 19.328, + "pct_cuda_time": 0.05630761694820816, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.328, + "pct_cuda_time": 0.05630761694820816, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 807.481, + "cuda_time_us": 30.367000000000004, + "pct_cuda_time": 0.08846716700466875, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.711, + "pct_cuda_time": 0.022464198793855195, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.12, + "pct_cuda_time": 0.06152819070499568, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004474777505817867, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 198.666, + "cuda_time_us": 80.22200000000001, + "pct_cuda_time": 0.23370807361440168, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 79.486, + "pct_cuda_time": 0.23156390939286395, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.697, + "cuda_time_us": 15.136, + "pct_cuda_time": 0.04409520333858023, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.136, + "pct_cuda_time": 0.04409520333858023, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 470.78, + "cuda_time_us": 782.006, + "pct_cuda_time": 2.278191964983468, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 165.919, + "cuda_time_us": 480.154, + "pct_cuda_time": 1.3988166136253075, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 479.418, + "pct_cuda_time": 1.3966724494037697, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.747, + "cuda_time_us": 66.943, + "pct_cuda_time": 0.19502280636195668, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 66.943, + "pct_cuda_time": 0.19502280636195668, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 150.848, + "cuda_time_us": 234.909, + "pct_cuda_time": 0.684352544996204, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.832, + "pct_cuda_time": 0.0024238378156513445, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 234.077, + "pct_cuda_time": 0.6819287071805527, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2635.803, + "cuda_time_us": 1054.192, + "pct_cuda_time": 3.0711423492273107, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.571, + "cuda_time_us": 14.367, + "pct_cuda_time": 0.041854901319065954, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.367, + "pct_cuda_time": 0.041854901319065954, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1876.371, + "cuda_time_us": 236.89100000000002, + "pct_cuda_time": 0.6901266394080081, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 154.048, + "cuda_time_us": 106.97500000000001, + "pct_cuda_time": 0.3116466951073349, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.239, + "pct_cuda_time": 0.30950253088579716, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 566.104, + "cuda_time_us": 19.647, + "pct_cuda_time": 0.05723694899531487, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.647, + "pct_cuda_time": 0.05723694899531487, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 778.949, + "cuda_time_us": 30.078, + "pct_cuda_time": 0.08762523295572254, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.552, + "pct_cuda_time": 0.022000989403604513, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.055, + "pct_cuda_time": 0.061338828375647914, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004285415176470106, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 221.477, + "cuda_time_us": 80.191, + "pct_cuda_time": 0.23361776234963583, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 79.455, + "pct_cuda_time": 0.23147359812809806, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 91.003, + "cuda_time_us": 15.072, + "pct_cuda_time": 0.04390875427583782, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.072, + "pct_cuda_time": 0.04390875427583782, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 500.376, + "cuda_time_us": 787.862, + "pct_cuda_time": 2.295252054224399, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 182.771, + "cuda_time_us": 483.83299999999997, + "pct_cuda_time": 1.4095345214663908, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 483.097, + "pct_cuda_time": 1.407390357244853, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.689, + "cuda_time_us": 67.424, + "pct_cuda_time": 0.19642408759913016, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.424, + "pct_cuda_time": 0.19642408759913016, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 157.501, + "cuda_time_us": 236.605, + "pct_cuda_time": 0.6892934451588779, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.832, + "pct_cuda_time": 0.0024238378156513445, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 235.773, + "pct_cuda_time": 0.6868696073432266, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2478.023, + "cuda_time_us": 1055.41, + "pct_cuda_time": 3.0746907079526276, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.866, + "cuda_time_us": 14.368, + "pct_cuda_time": 0.0418578145856713, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.368, + "pct_cuda_time": 0.0418578145856713, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1792.65, + "cuda_time_us": 240.668, + "pct_cuda_time": 0.7011300473764157, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 156.119, + "cuda_time_us": 107.775, + "pct_cuda_time": 0.31397730839161503, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 107.039, + "pct_cuda_time": 0.3118331441700773, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 511.831, + "cuda_time_us": 19.84, + "pct_cuda_time": 0.05779920945014745, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.84, + "pct_cuda_time": 0.05779920945014745, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 737.381, + "cuda_time_us": 30.047, + "pct_cuda_time": 0.08753492169095668, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.68, + "pct_cuda_time": 0.022373887529089337, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.055, + "pct_cuda_time": 0.061338828375647914, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0038222057862194285, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 234.635, + "cuda_time_us": 83.006, + "pct_cuda_time": 0.24181860784369655, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 82.27, + "pct_cuda_time": 0.2396744436221588, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.765, + "cuda_time_us": 15.808, + "pct_cuda_time": 0.04605291849737555, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.808, + "pct_cuda_time": 0.04605291849737555, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 455.932, + "cuda_time_us": 784.566, + "pct_cuda_time": 2.2856499274931648, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.216, + "cuda_time_us": 481.786, + "pct_cuda_time": 1.4035710647252388, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 481.05, + "pct_cuda_time": 1.4014269005037012, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.157, + "cuda_time_us": 67.327, + "pct_cuda_time": 0.1961415007384112, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.327, + "pct_cuda_time": 0.1961415007384112, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 150.195, + "cuda_time_us": 235.453, + "pct_cuda_time": 0.6859373620295145, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.832, + "pct_cuda_time": 0.0024238378156513445, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 234.621, + "pct_cuda_time": 0.6835135242138632, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2416.184, + "cuda_time_us": 1051.9560000000001, + "pct_cuda_time": 3.064628285097748, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.036, + "cuda_time_us": 15.008, + "pct_cuda_time": 0.04372230521309541, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.008, + "pct_cuda_time": 0.04372230521309541, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1695.681, + "cuda_time_us": 238.399, + "pct_cuda_time": 0.6945198454488761, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.343, + "cuda_time_us": 107.42200000000001, + "pct_cuda_time": 0.3129489252799264, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.686, + "pct_cuda_time": 0.31080476105838867, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 479.894, + "cuda_time_us": 19.168, + "pct_cuda_time": 0.055841494291352135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.168, + "pct_cuda_time": 0.055841494291352135, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 744.814, + "cuda_time_us": 29.985, + "pct_cuda_time": 0.08735429916142497, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.52, + "pct_cuda_time": 0.021907764872233306, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.152, + "pct_cuda_time": 0.061621415236366876, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0038251190528247785, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 182.436, + "cuda_time_us": 81.824, + "pct_cuda_time": 0.23837512671617261, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 81.056, + "pct_cuda_time": 0.2361377379632637, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.289, + "cuda_time_us": 15.552, + "pct_cuda_time": 0.0453071222464059, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.552, + "pct_cuda_time": 0.0453071222464059, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 491.892, + "cuda_time_us": 782.9970000000001, + "pct_cuda_time": 2.2810790121893705, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 156.995, + "cuda_time_us": 480.954, + "pct_cuda_time": 1.4011472269095877, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 480.218, + "pct_cuda_time": 1.3990030626880499, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.77, + "cuda_time_us": 66.463, + "pct_cuda_time": 0.1936244383913886, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 66.463, + "pct_cuda_time": 0.1936244383913886, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 158.336, + "cuda_time_us": 235.58, + "pct_cuda_time": 0.686307346888394, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.799, + "pct_cuda_time": 0.0023277000176747894, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 234.781, + "pct_cuda_time": 0.6839796468707192, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2834.985, + "cuda_time_us": 1052.2089999999998, + "pct_cuda_time": 3.0653653415489006, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.729, + "cuda_time_us": 14.688, + "pct_cuda_time": 0.04279005989938336, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.688, + "pct_cuda_time": 0.04279005989938336, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2113.744, + "cuda_time_us": 236.413, + "pct_cuda_time": 0.6887340979706508, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 147.272, + "cuda_time_us": 107.16600000000001, + "pct_cuda_time": 0.3122031290289568, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.43, + "pct_cuda_time": 0.310058964807419, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 754.139, + "cuda_time_us": 19.232, + "pct_cuda_time": 0.05602794335409454, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.232, + "pct_cuda_time": 0.05602794335409454, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 808.684, + "cuda_time_us": 30.464000000000002, + "pct_cuda_time": 0.0887497538653877, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.52, + "pct_cuda_time": 0.021907764872233306, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.44, + "pct_cuda_time": 0.06246043601870774, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004381552974446662, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 209.516, + "cuda_time_us": 79.551, + "pct_cuda_time": 0.2317532717222117, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 78.815, + "pct_cuda_time": 0.22960910750067395, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.239, + "cuda_time_us": 14.88, + "pct_cuda_time": 0.04334940708761059, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.88, + "pct_cuda_time": 0.04334940708761059, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.551, + "cuda_time_us": 786.228, + "pct_cuda_time": 2.2904917765912565, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 170.719, + "cuda_time_us": 483.993, + "pct_cuda_time": 1.4100006441232467, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 483.257, + "pct_cuda_time": 1.407856479901709, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.779, + "cuda_time_us": 66.591, + "pct_cuda_time": 0.19399733651687343, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 66.591, + "pct_cuda_time": 0.19399733651687343, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.334, + "cuda_time_us": 235.644, + "pct_cuda_time": 0.6864937959511365, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 234.844, + "pct_cuda_time": 0.6841631826668563, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2399.425, + "cuda_time_us": 1053.652, + "pct_cuda_time": 3.0695691852604217, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.754, + "cuda_time_us": 14.56, + "pct_cuda_time": 0.04241716177389853, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.56, + "pct_cuda_time": 0.04241716177389853, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1717.133, + "cuda_time_us": 236.99, + "pct_cuda_time": 0.6904150528019378, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.75, + "cuda_time_us": 107.199, + "pct_cuda_time": 0.31229926682693326, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.463, + "pct_cuda_time": 0.3101551026053956, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 512.157, + "cuda_time_us": 19.392, + "pct_cuda_time": 0.05649406601095057, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.392, + "pct_cuda_time": 0.05649406601095057, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 734.419, + "cuda_time_us": 30.048000000000002, + "pct_cuda_time": 0.08753783495756202, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.616, + "pct_cuda_time": 0.022187438466346923, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 20.832, + "pct_cuda_time": 0.06068916992265482, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004661226568560279, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 182.293, + "cuda_time_us": 80.351, + "pct_cuda_time": 0.23408388500649183, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 79.615, + "pct_cuda_time": 0.2319397207849541, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 92.292, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.045493571309148315, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.045493571309148315, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 454.484, + "cuda_time_us": 786.486, + "pct_cuda_time": 2.291243399375437, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 153.713, + "cuda_time_us": 483.258, + "pct_cuda_time": 1.4078593931683143, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 482.522, + "pct_cuda_time": 1.4057152289467765, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.843, + "cuda_time_us": 67.232, + "pct_cuda_time": 0.19586474041090288, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.232, + "pct_cuda_time": 0.19586474041090288, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 150.754, + "cuda_time_us": 235.996, + "pct_cuda_time": 0.6875192657962197, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 235.196, + "pct_cuda_time": 0.6851886525119396, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2478.503, + "cuda_time_us": 1056.115, + "pct_cuda_time": 3.076744560909399, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.27, + "cuda_time_us": 15.007, + "pct_cuda_time": 0.043719391946490056, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.007, + "pct_cuda_time": 0.043719391946490056, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1751.626, + "cuda_time_us": 239.582, + "pct_cuda_time": 0.6979662398430054, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.327, + "cuda_time_us": 106.783, + "pct_cuda_time": 0.31108734791910764, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.015, + "pct_cuda_time": 0.3088499591661987, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 513.889, + "cuda_time_us": 19.04, + "pct_cuda_time": 0.055468596165867315, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.04, + "pct_cuda_time": 0.055468596165867315, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 739.19, + "cuda_time_us": 30.048000000000002, + "pct_cuda_time": 0.08753783495756202, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.616, + "pct_cuda_time": 0.022187438466346923, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.184, + "pct_cuda_time": 0.06171463976773809, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.248, + "pct_cuda_time": 0.0036357567234770174, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 207.424, + "cuda_time_us": 83.711, + "pct_cuda_time": 0.2438724608004684, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 82.975, + "pct_cuda_time": 0.2417282965789307, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.585, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.04558679584051952, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.04558679584051952, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 492.829, + "cuda_time_us": 785.878, + "pct_cuda_time": 2.289472133279384, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 180.25, + "cuda_time_us": 481.978, + "pct_cuda_time": 1.404130411913466, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 481.242, + "pct_cuda_time": 1.4019862476919285, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.866, + "cuda_time_us": 67.647, + "pct_cuda_time": 0.19707374605212324, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.647, + "pct_cuda_time": 0.19707374605212324, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 156.41, + "cuda_time_us": 236.25300000000001, + "pct_cuda_time": 0.6882679753137947, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 235.453, + "pct_cuda_time": 0.6859373620295145, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2541.517, + "cuda_time_us": 1053.297, + "pct_cuda_time": 3.0685349756155222, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.993, + "cuda_time_us": 14.4, + "pct_cuda_time": 0.0419510391170425, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.4, + "pct_cuda_time": 0.0419510391170425, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1759.337, + "cuda_time_us": 237.692, + "pct_cuda_time": 0.6924601659588936, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 166.408, + "cuda_time_us": 106.783, + "pct_cuda_time": 0.31108734791910764, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.047, + "pct_cuda_time": 0.3089431836975699, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 498.468, + "cuda_time_us": 19.2, + "pct_cuda_time": 0.055934718822723335, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.2, + "pct_cuda_time": 0.055934718822723335, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 756.463, + "cuda_time_us": 29.727, + "pct_cuda_time": 0.08660267637724463, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.456, + "pct_cuda_time": 0.0217213158094909, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 20.927, + "pct_cuda_time": 0.060965930250163086, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0039154303175906345, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 190.862, + "cuda_time_us": 81.982, + "pct_cuda_time": 0.23883542283981798, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.002141250954932378, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 81.247, + "pct_cuda_time": 0.2366941718848856, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.569, + "cuda_time_us": 15.136, + "pct_cuda_time": 0.04409520333858023, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.136, + "pct_cuda_time": 0.04409520333858023, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 558.516, + "cuda_time_us": 786.069, + "pct_cuda_time": 2.2900285672010057, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.249, + "cuda_time_us": 483.32099999999997, + "pct_cuda_time": 1.4080429289644514, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.767, + "pct_cuda_time": 0.002234475486303584, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 482.554, + "pct_cuda_time": 1.4058084534781479, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.245, + "cuda_time_us": 67.167, + "pct_cuda_time": 0.19567537808155513, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.167, + "pct_cuda_time": 0.19567537808155513, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 252.646, + "cuda_time_us": 235.58100000000002, + "pct_cuda_time": 0.6863102601549994, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 234.781, + "pct_cuda_time": 0.6839796468707192, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2483.847, + "cuda_time_us": 1054.896, + "pct_cuda_time": 3.0731932889174773, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.838, + "cuda_time_us": 14.784, + "pct_cuda_time": 0.04306973349349697, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.784, + "pct_cuda_time": 0.04306973349349697, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1761.451, + "cuda_time_us": 239.642, + "pct_cuda_time": 0.6981410358393264, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 154.659, + "cuda_time_us": 107.261, + "pct_cuda_time": 0.312479889356465, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.002141250954932378, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.526, + "pct_cuda_time": 0.31033863840153264, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 527.009, + "cuda_time_us": 19.52, + "pct_cuda_time": 0.0568669641364354, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.52, + "pct_cuda_time": 0.0568669641364354, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 742.031, + "cuda_time_us": 30.367, + "pct_cuda_time": 0.08846716700466874, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.776, + "pct_cuda_time": 0.02265356112320295, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.087, + "pct_cuda_time": 0.06143205290701912, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004381552974446662, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 187.135, + "cuda_time_us": 82.494, + "pct_cuda_time": 0.24032701534175724, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.002141250954932378, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 81.759, + "pct_cuda_time": 0.2381857643868249, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 95.237, + "cuda_time_us": 15.615, + "pct_cuda_time": 0.045490658042542965, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.615, + "pct_cuda_time": 0.045490658042542965, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 484.342, + "cuda_time_us": 784.8549999999999, + "pct_cuda_time": 2.2864918615421104, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.519, + "cuda_time_us": 482.45799999999997, + "pct_cuda_time": 1.4055287798840344, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 481.69, + "pct_cuda_time": 1.4032913911311253, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.348, + "cuda_time_us": 66.752, + "pct_cuda_time": 0.1944663724403348, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 66.752, + "pct_cuda_time": 0.1944663724403348, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 175.067, + "cuda_time_us": 235.645, + "pct_cuda_time": 0.6864967092177419, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 234.845, + "pct_cuda_time": 0.6841660959334617, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2389.865, + "cuda_time_us": 1049.843, + "pct_cuda_time": 3.058472552760643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.894, + "cuda_time_us": 14.624, + "pct_cuda_time": 0.042603610836640944, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.624, + "pct_cuda_time": 0.042603610836640944, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1719.155, + "cuda_time_us": 235.58100000000002, + "pct_cuda_time": 0.6863102601549994, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 158.969, + "cuda_time_us": 106.941, + "pct_cuda_time": 0.311547644042753, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.002141250954932378, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.206, + "pct_cuda_time": 0.3094063930878206, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 493.421, + "cuda_time_us": 19.297, + "pct_cuda_time": 0.05621730568344231, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.297, + "pct_cuda_time": 0.05621730568344231, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 749.37, + "cuda_time_us": 29.92, + "pct_cuda_time": 0.08716493683207721, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.424, + "pct_cuda_time": 0.021628091278119693, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.024, + "pct_cuda_time": 0.06124851711088206, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004288328443075456, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 181.199, + "cuda_time_us": 79.423, + "pct_cuda_time": 0.23138037359672686, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 78.687, + "pct_cuda_time": 0.22923620937518913, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.3, + "cuda_time_us": 15.679, + "pct_cuda_time": 0.04567710710528538, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.679, + "pct_cuda_time": 0.04567710710528538, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 452.604, + "cuda_time_us": 783.9590000000001, + "pct_cuda_time": 2.2838815746637176, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 152.624, + "cuda_time_us": 481.46700000000004, + "pct_cuda_time": 1.4026417326781324, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0021470774881430783, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 480.73, + "pct_cuda_time": 1.4004946551899893, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.722, + "cuda_time_us": 67.103, + "pct_cuda_time": 0.1954889290188127, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.103, + "pct_cuda_time": 0.1954889290188127, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.744, + "cuda_time_us": 235.389, + "pct_cuda_time": 0.6857509129667722, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 234.589, + "pct_cuda_time": 0.683420299682492, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2411.619, + "cuda_time_us": 1056.34, + "pct_cuda_time": 3.0774000458956023, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.243, + "cuda_time_us": 14.688, + "pct_cuda_time": 0.04279005989938336, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.688, + "pct_cuda_time": 0.04279005989938336, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1752.291, + "cuda_time_us": 239.966, + "pct_cuda_time": 0.6990849342194598, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.008, + "cuda_time_us": 106.97500000000001, + "pct_cuda_time": 0.3116466951073349, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.239, + "pct_cuda_time": 0.30950253088579716, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 468.663, + "cuda_time_us": 19.232, + "pct_cuda_time": 0.05602794335409454, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.232, + "pct_cuda_time": 0.05602794335409454, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 721.762, + "cuda_time_us": 30.048000000000002, + "pct_cuda_time": 0.08753783495756202, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.712, + "pct_cuda_time": 0.022467112060460544, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 20.992, + "pct_cuda_time": 0.061155292579510856, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0039154303175906345, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 189.521, + "cuda_time_us": 83.711, + "pct_cuda_time": 0.2438724608004684, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 82.975, + "pct_cuda_time": 0.2417282965789307, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.979, + "cuda_time_us": 15.552, + "pct_cuda_time": 0.0453071222464059, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.552, + "pct_cuda_time": 0.0453071222464059, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 443.311, + "cuda_time_us": 786.1339999999999, + "pct_cuda_time": 2.2902179295303533, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 153.269, + "cuda_time_us": 482.554, + "pct_cuda_time": 1.4058084534781479, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 481.818, + "pct_cuda_time": 1.40366428925661, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.762, + "cuda_time_us": 67.103, + "pct_cuda_time": 0.1954889290188127, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.103, + "pct_cuda_time": 0.1954889290188127, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.403, + "cuda_time_us": 236.477, + "pct_cuda_time": 0.6889205470333931, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 235.677, + "pct_cuda_time": 0.6865899337491129, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2644.842, + "cuda_time_us": 1050.451, + "pct_cuda_time": 3.0602438188566956, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.421, + "cuda_time_us": 14.336, + "pct_cuda_time": 0.041764590054300096, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.336, + "pct_cuda_time": 0.041764590054300096, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1944.342, + "cuda_time_us": 237.34199999999998, + "pct_cuda_time": 0.6914405226470209, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.851, + "cuda_time_us": 107.007, + "pct_cuda_time": 0.3117399196387061, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.271, + "pct_cuda_time": 0.30959575541716833, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 483.885, + "cuda_time_us": 19.584, + "pct_cuda_time": 0.05705341319917781, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.584, + "pct_cuda_time": 0.05705341319917781, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 965.253, + "cuda_time_us": 29.76, + "pct_cuda_time": 0.08669881417522118, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.488, + "pct_cuda_time": 0.021814540340862103, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 20.96, + "pct_cuda_time": 0.06106206804813965, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0038222057862194285, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 216.155, + "cuda_time_us": 80.991, + "pct_cuda_time": 0.23594837563391596, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 80.255, + "pct_cuda_time": 0.23380421141237817, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 91.085, + "cuda_time_us": 15.327, + "pct_cuda_time": 0.04465163726020212, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.327, + "pct_cuda_time": 0.04465163726020212, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 472.021, + "cuda_time_us": 783.446, + "pct_cuda_time": 2.2823870688951726, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 161.825, + "cuda_time_us": 481.04999999999995, + "pct_cuda_time": 1.401426900503701, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 480.282, + "pct_cuda_time": 1.3991895117507922, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.572, + "cuda_time_us": 67.263, + "pct_cuda_time": 0.1959550516756688, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.263, + "pct_cuda_time": 0.1959550516756688, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 160.721, + "cuda_time_us": 235.133, + "pct_cuda_time": 0.6850051167158024, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 234.333, + "pct_cuda_time": 0.6826745034315223, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2496.697, + "cuda_time_us": 1055.346, + "pct_cuda_time": 3.0745042588898848, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.171, + "cuda_time_us": 14.559, + "pct_cuda_time": 0.04241424850729318, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.559, + "pct_cuda_time": 0.04241424850729318, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1791.467, + "cuda_time_us": 240.03, + "pct_cuda_time": 0.6992713832822023, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 173.45, + "cuda_time_us": 106.559, + "pct_cuda_time": 0.3104347761995092, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 105.823, + "pct_cuda_time": 0.3082906119779715, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 550.63, + "cuda_time_us": 19.872, + "pct_cuda_time": 0.05789243398151866, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.872, + "pct_cuda_time": 0.05789243398151866, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 733.565, + "cuda_time_us": 30.240000000000002, + "pct_cuda_time": 0.08809718214578927, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.616, + "pct_cuda_time": 0.022187438466346923, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.12, + "pct_cuda_time": 0.06152819070499568, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004381552974446662, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 186.682, + "cuda_time_us": 83.35900000000001, + "pct_cuda_time": 0.2428469909553852, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 82.623, + "pct_cuda_time": 0.24070282673384746, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.314, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.045493571309148315, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.045493571309148315, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 483.89, + "cuda_time_us": 785.1410000000001, + "pct_cuda_time": 2.287325055791241, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 186.646, + "cuda_time_us": 481.529, + "pct_cuda_time": 1.402822355207664, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 480.793, + "pct_cuda_time": 1.4006781909861261, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.208, + "cuda_time_us": 67.455, + "pct_cuda_time": 0.19651439886389596, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.455, + "pct_cuda_time": 0.19651439886389596, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 150.559, + "cuda_time_us": 236.15699999999998, + "pct_cuda_time": 0.687988301719681, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.832, + "pct_cuda_time": 0.0024238378156513445, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 235.325, + "pct_cuda_time": 0.6855644639040297, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2407.003, + "cuda_time_us": 1053.776, + "pct_cuda_time": 3.069930430319485, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.123, + "cuda_time_us": 14.432, + "pct_cuda_time": 0.04204426364841371, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.432, + "pct_cuda_time": 0.04204426364841371, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1743.931, + "cuda_time_us": 238.45999999999998, + "pct_cuda_time": 0.6946975547118025, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.67, + "cuda_time_us": 106.846, + "pct_cuda_time": 0.3112708837152447, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.11, + "pct_cuda_time": 0.30912671949370696, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 494.719, + "cuda_time_us": 20.159, + "pct_cuda_time": 0.05872854149725416, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 20.159, + "pct_cuda_time": 0.05872854149725416, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 724.351, + "cuda_time_us": 30.208000000000002, + "pct_cuda_time": 0.08800395761441807, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.744, + "pct_cuda_time": 0.022560336591831747, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 20.768, + "pct_cuda_time": 0.060502720859912415, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004940900162673895, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 203.18, + "cuda_time_us": 81.247, + "pct_cuda_time": 0.2366941718848856, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 80.479, + "pct_cuda_time": 0.23445678313197665, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.663, + "cuda_time_us": 15.744, + "pct_cuda_time": 0.045866469434633135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.744, + "pct_cuda_time": 0.045866469434633135, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 444.012, + "cuda_time_us": 785.1400000000001, + "pct_cuda_time": 2.2873221425246357, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 154.213, + "cuda_time_us": 482.105, + "pct_cuda_time": 1.4045003967723457, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 481.369, + "pct_cuda_time": 1.402356232550808, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.168, + "cuda_time_us": 66.879, + "pct_cuda_time": 0.19483635729921428, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 66.879, + "pct_cuda_time": 0.19483635729921428, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.522, + "cuda_time_us": 236.156, + "pct_cuda_time": 0.6879853884530757, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.832, + "pct_cuda_time": 0.0024238378156513445, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 235.324, + "pct_cuda_time": 0.6855615506374244, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2372.44, + "cuda_time_us": 1055.859, + "pct_cuda_time": 3.0759987646584293, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.164, + "cuda_time_us": 14.496, + "pct_cuda_time": 0.042230712711156124, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.496, + "pct_cuda_time": 0.042230712711156124, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1667.833, + "cuda_time_us": 240.382, + "pct_cuda_time": 0.7002968531272855, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.932, + "cuda_time_us": 107.359, + "pct_cuda_time": 0.31276538948378935, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.591, + "pct_cuda_time": 0.3105280007308804, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 474.974, + "cuda_time_us": 19.263, + "pct_cuda_time": 0.0561182546188604, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.263, + "pct_cuda_time": 0.0561182546188604, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 711.572, + "cuda_time_us": 30.176000000000002, + "pct_cuda_time": 0.08791073308304685, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.616, + "pct_cuda_time": 0.022187438466346923, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.28, + "pct_cuda_time": 0.061994313361851704, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.003728981254848223, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 193.141, + "cuda_time_us": 83.584, + "pct_cuda_time": 0.24350247594158897, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 82.848, + "pct_cuda_time": 0.24135831172005123, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.154, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.04558679584051952, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.04558679584051952, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 478.874, + "cuda_time_us": 785.333, + "pct_cuda_time": 2.287884402979468, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 158.442, + "cuda_time_us": 482.937, + "pct_cuda_time": 1.4069242345879969, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 482.201, + "pct_cuda_time": 1.4047800703664592, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 103.301, + "cuda_time_us": 67.199, + "pct_cuda_time": 0.19576860261292633, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.199, + "pct_cuda_time": 0.19576860261292633, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 165.259, + "cuda_time_us": 235.197, + "pct_cuda_time": 0.6851915657785449, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 234.397, + "pct_cuda_time": 0.6828609524942647, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2434.86, + "cuda_time_us": 1050.194, + "pct_cuda_time": 3.0594951093391205, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.706, + "cuda_time_us": 14.432, + "pct_cuda_time": 0.04204426364841371, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.432, + "pct_cuda_time": 0.04204426364841371, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1768.644, + "cuda_time_us": 235.86900000000003, + "pct_cuda_time": 0.6871492809373403, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 161.498, + "cuda_time_us": 106.494, + "pct_cuda_time": 0.3102454138701614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 105.726, + "pct_cuda_time": 0.3080080251172525, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 541.413, + "cuda_time_us": 19.328, + "pct_cuda_time": 0.05630761694820816, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.328, + "pct_cuda_time": 0.05630761694820816, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 725.691, + "cuda_time_us": 29.984, + "pct_cuda_time": 0.08735138589481962, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.584, + "pct_cuda_time": 0.02209421393497572, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.088, + "pct_cuda_time": 0.06143496617362447, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0038222057862194285, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 193.582, + "cuda_time_us": 80.063, + "pct_cuda_time": 0.233244864224151, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 79.327, + "pct_cuda_time": 0.23110070000261326, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.434, + "cuda_time_us": 15.072, + "pct_cuda_time": 0.04390875427583782, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.072, + "pct_cuda_time": 0.04390875427583782, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 448.191, + "cuda_time_us": 784.8209999999999, + "pct_cuda_time": 2.286392810477529, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 154.07, + "cuda_time_us": 482.137, + "pct_cuda_time": 1.404593621303717, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 481.401, + "pct_cuda_time": 1.402449457082179, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.039, + "cuda_time_us": 67.071, + "pct_cuda_time": 0.1953957044874415, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.071, + "pct_cuda_time": 0.1953957044874415, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.936, + "cuda_time_us": 235.613, + "pct_cuda_time": 0.6864034846863706, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 234.813, + "pct_cuda_time": 0.6840728714020904, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2481.461, + "cuda_time_us": 1058.1309999999999, + "pct_cuda_time": 3.0826177063857845, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.079, + "cuda_time_us": 15.04, + "pct_cuda_time": 0.04381552974446661, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.04, + "pct_cuda_time": 0.04381552974446661, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1714.989, + "cuda_time_us": 241.277, + "pct_cuda_time": 0.7029042267390739, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 148.021, + "cuda_time_us": 107.006, + "pct_cuda_time": 0.3117370063721007, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.238, + "pct_cuda_time": 0.30949961761919176, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 492.05, + "cuda_time_us": 19.392, + "pct_cuda_time": 0.05649406601095057, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.392, + "pct_cuda_time": 0.05649406601095057, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 734.698, + "cuda_time_us": 30.112000000000002, + "pct_cuda_time": 0.08772428402030445, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.552, + "pct_cuda_time": 0.022000989403604513, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.088, + "pct_cuda_time": 0.06143496617362447, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004288328443075456, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 199.549, + "cuda_time_us": 84.76700000000001, + "pct_cuda_time": 0.24694887033571822, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 84.031, + "pct_cuda_time": 0.24480470611418048, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 99.798, + "cuda_time_us": 15.104, + "pct_cuda_time": 0.044001978807209026, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.104, + "pct_cuda_time": 0.044001978807209026, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 521.111, + "cuda_time_us": 786.7099999999999, + "pct_cuda_time": 2.2918959710950353, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.372, + "cuda_time_us": 482.87399999999997, + "pct_cuda_time": 1.4067406987918598, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 482.138, + "pct_cuda_time": 1.4045965345703222, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 132.36, + "cuda_time_us": 66.815, + "pct_cuda_time": 0.19464990823647188, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 66.815, + "pct_cuda_time": 0.19464990823647188, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 173.841, + "cuda_time_us": 237.021, + "pct_cuda_time": 0.6905053640667036, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.832, + "pct_cuda_time": 0.0024238378156513445, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 236.189, + "pct_cuda_time": 0.6880815262510522, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2431.228, + "cuda_time_us": 1057.011, + "pct_cuda_time": 3.0793548477877923, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.082, + "cuda_time_us": 14.72, + "pct_cuda_time": 0.042883284430754565, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.72, + "pct_cuda_time": 0.042883284430754565, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1739.264, + "cuda_time_us": 239.54899999999998, + "pct_cuda_time": 0.6978701020450288, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 153.234, + "cuda_time_us": 106.367, + "pct_cuda_time": 0.30987542901128196, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 105.599, + "pct_cuda_time": 0.30763804025837305, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 526.556, + "cuda_time_us": 19.327, + "pct_cuda_time": 0.05630470368160282, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.327, + "pct_cuda_time": 0.05630470368160282, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 728.092, + "cuda_time_us": 30.495, + "pct_cuda_time": 0.08884006513015356, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.679, + "pct_cuda_time": 0.022370974262483988, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.376, + "pct_cuda_time": 0.062273986955965324, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004195103911704251, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 182.099, + "cuda_time_us": 83.36, + "pct_cuda_time": 0.2428499042219905, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 1.952, + "pct_cuda_time": 0.005686696413643539, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 81.408, + "pct_cuda_time": 0.23716320780834696, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.504, + "cuda_time_us": 15.808, + "pct_cuda_time": 0.04605291849737555, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.808, + "pct_cuda_time": 0.04605291849737555, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 460.967, + "cuda_time_us": 786.934, + "pct_cuda_time": 2.2925485428146337, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 168.064, + "cuda_time_us": 482.938, + "pct_cuda_time": 1.4069271478546024, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 482.202, + "pct_cuda_time": 1.4047829836330648, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.095, + "cuda_time_us": 67.327, + "pct_cuda_time": 0.1961415007384112, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.327, + "pct_cuda_time": 0.1961415007384112, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 147.79, + "cuda_time_us": 236.669, + "pct_cuda_time": 0.6894798942216204, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 235.869, + "pct_cuda_time": 0.6871492809373402, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2730.716, + "cuda_time_us": 1055.156, + "pct_cuda_time": 3.073950738234868, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.278, + "cuda_time_us": 14.528, + "pct_cuda_time": 0.04232393724252733, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.528, + "pct_cuda_time": 0.04232393724252733, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 2049.143, + "cuda_time_us": 239.325, + "pct_cuda_time": 0.6972175303254303, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 149.311, + "cuda_time_us": 107.262, + "pct_cuda_time": 0.3124828026230703, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.494, + "pct_cuda_time": 0.3102454138701614, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 486.657, + "cuda_time_us": 19.04, + "pct_cuda_time": 0.055468596165867315, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.04, + "pct_cuda_time": 0.055468596165867315, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1078.095, + "cuda_time_us": 29.761, + "pct_cuda_time": 0.08670172744182653, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.584, + "pct_cuda_time": 0.02209421393497572, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 20.897, + "pct_cuda_time": 0.06087853225200258, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.003728981254848223, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 199.036, + "cuda_time_us": 83.262, + "pct_cuda_time": 0.2425644040946662, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.002141250954932378, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 82.527, + "pct_cuda_time": 0.2404231531397338, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.047, + "cuda_time_us": 15.872, + "pct_cuda_time": 0.04623936756011796, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.872, + "pct_cuda_time": 0.04623936756011796, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 454.001, + "cuda_time_us": 785.431, + "pct_cuda_time": 2.288169903106793, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 152.174, + "cuda_time_us": 482.363, + "pct_cuda_time": 1.405252019556526, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0021470774881430783, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 481.626, + "pct_cuda_time": 1.4031049420683828, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.628, + "cuda_time_us": 66.655, + "pct_cuda_time": 0.19418378557961585, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 66.655, + "pct_cuda_time": 0.19418378557961585, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.764, + "cuda_time_us": 236.413, + "pct_cuda_time": 0.6887340979706508, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 235.613, + "pct_cuda_time": 0.6864034846863706, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2486.154, + "cuda_time_us": 1051.8890000000001, + "pct_cuda_time": 3.06443309623519, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.857, + "cuda_time_us": 14.272, + "pct_cuda_time": 0.04157814099155768, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.272, + "pct_cuda_time": 0.04157814099155768, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1776.623, + "cuda_time_us": 237.72500000000002, + "pct_cuda_time": 0.6925563037568702, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 145.528, + "cuda_time_us": 106.81400000000001, + "pct_cuda_time": 0.3111776591838735, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.046, + "pct_cuda_time": 0.30894027043096456, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 522.524, + "cuda_time_us": 19.712, + "pct_cuda_time": 0.057426311324662624, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.712, + "pct_cuda_time": 0.057426311324662624, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 740.673, + "cuda_time_us": 30.208000000000002, + "pct_cuda_time": 0.08800395761441807, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.936, + "pct_cuda_time": 0.02311968378005898, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 20.992, + "pct_cuda_time": 0.061155292579510856, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.003728981254848223, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 217.37, + "cuda_time_us": 80.991, + "pct_cuda_time": 0.23594837563391596, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 80.255, + "pct_cuda_time": 0.23380421141237817, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 97.909, + "cuda_time_us": 15.359, + "pct_cuda_time": 0.04474486179157332, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.359, + "pct_cuda_time": 0.04474486179157332, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 473.543, + "cuda_time_us": 784.5330000000001, + "pct_cuda_time": 2.2855537896951885, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 164.325, + "cuda_time_us": 482.39300000000003, + "pct_cuda_time": 1.4053394175546865, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.002141250954932378, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 481.658, + "pct_cuda_time": 1.4031981665997542, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.241, + "cuda_time_us": 66.623, + "pct_cuda_time": 0.19409056104824465, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 66.623, + "pct_cuda_time": 0.19409056104824465, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 156.94, + "cuda_time_us": 235.51700000000002, + "pct_cuda_time": 0.6861238110922571, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 234.717, + "pct_cuda_time": 0.6837931978079769, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2561.769, + "cuda_time_us": 1055.7640000000001, + "pct_cuda_time": 3.0757220043309217, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.052, + "cuda_time_us": 14.721, + "pct_cuda_time": 0.042886197697359914, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.721, + "pct_cuda_time": 0.042886197697359914, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1813.829, + "cuda_time_us": 239.293, + "pct_cuda_time": 0.6971243057940592, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 184.594, + "cuda_time_us": 106.91, + "pct_cuda_time": 0.3114573327779871, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.002141250954932378, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.175, + "pct_cuda_time": 0.30931608182305476, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 556.225, + "cuda_time_us": 19.552, + "pct_cuda_time": 0.056960188667806604, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.552, + "pct_cuda_time": 0.056960188667806604, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 733.292, + "cuda_time_us": 30.336000000000002, + "pct_cuda_time": 0.0883768557399029, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.584, + "pct_cuda_time": 0.02209421393497572, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.248, + "pct_cuda_time": 0.0619010888304805, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004381552974446662, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 192.525, + "cuda_time_us": 82.495, + "pct_cuda_time": 0.2403299286083626, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 81.759, + "pct_cuda_time": 0.2381857643868249, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.512, + "cuda_time_us": 15.808, + "pct_cuda_time": 0.04605291849737555, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.808, + "pct_cuda_time": 0.04605291849737555, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 458.643, + "cuda_time_us": 785.942, + "pct_cuda_time": 2.2896585823421267, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 154.628, + "cuda_time_us": 482.618, + "pct_cuda_time": 1.4059949025408902, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 481.882, + "pct_cuda_time": 1.4038507383193526, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.893, + "cuda_time_us": 67.36, + "pct_cuda_time": 0.1962376385363877, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.36, + "pct_cuda_time": 0.1962376385363877, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 157.532, + "cuda_time_us": 235.964, + "pct_cuda_time": 0.6874260412648485, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 235.164, + "pct_cuda_time": 0.6850954279805683, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2402.776, + "cuda_time_us": 1056.8500000000001, + "pct_cuda_time": 3.078885811864332, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.892, + "cuda_time_us": 14.304, + "pct_cuda_time": 0.04167136552292889, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.304, + "pct_cuda_time": 0.04167136552292889, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1705.411, + "cuda_time_us": 240.82799999999997, + "pct_cuda_time": 0.7015961700332717, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 147.972, + "cuda_time_us": 107.423, + "pct_cuda_time": 0.31295183854653175, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.655, + "pct_cuda_time": 0.31071444979362284, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 472.724, + "cuda_time_us": 19.584, + "pct_cuda_time": 0.05705341319917781, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.584, + "pct_cuda_time": 0.05705341319917781, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 738.627, + "cuda_time_us": 30.111, + "pct_cuda_time": 0.08772137075369908, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.679, + "pct_cuda_time": 0.022370974262483988, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 20.928, + "pct_cuda_time": 0.06096884351676845, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004381552974446662, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 201.701, + "cuda_time_us": 83.71, + "pct_cuda_time": 0.24386954753386303, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.704, + "pct_cuda_time": 0.0020509396901665223, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 83.006, + "pct_cuda_time": 0.24181860784369655, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.079, + "cuda_time_us": 15.04, + "pct_cuda_time": 0.04381552974446661, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.04, + "pct_cuda_time": 0.04381552974446661, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 468.839, + "cuda_time_us": 786.6780000000001, + "pct_cuda_time": 2.2918027465636643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.46, + "cuda_time_us": 483.706, + "pct_cuda_time": 1.4091645366075112, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 482.97, + "pct_cuda_time": 1.4070203723859736, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.359, + "cuda_time_us": 67.167, + "pct_cuda_time": 0.19567537808155513, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.167, + "pct_cuda_time": 0.19567537808155513, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 150.689, + "cuda_time_us": 235.805, + "pct_cuda_time": 0.6869628318745978, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 235.005, + "pct_cuda_time": 0.6846322185903176, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2435.914, + "cuda_time_us": 1057.745, + "pct_cuda_time": 3.08149318547612, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.268, + "cuda_time_us": 14.656, + "pct_cuda_time": 0.04269683536801215, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.656, + "pct_cuda_time": 0.04269683536801215, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1759.623, + "cuda_time_us": 239.228, + "pct_cuda_time": 0.6969349434647115, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 153.994, + "cuda_time_us": 106.494, + "pct_cuda_time": 0.3102454138701614, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 105.758, + "pct_cuda_time": 0.3081012496486237, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 553.029, + "cuda_time_us": 19.488, + "pct_cuda_time": 0.0567737396050642, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.488, + "pct_cuda_time": 0.0567737396050642, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 719.15, + "cuda_time_us": 30.527, + "pct_cuda_time": 0.08893328966152476, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.776, + "pct_cuda_time": 0.02265356112320295, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.471, + "pct_cuda_time": 0.06255074728347358, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.003728981254848223, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 184.83, + "cuda_time_us": 82.719, + "pct_cuda_time": 0.240982500327961, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 81.951, + "pct_cuda_time": 0.23874511157505207, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.312, + "cuda_time_us": 15.327, + "pct_cuda_time": 0.04465163726020212, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.327, + "pct_cuda_time": 0.04465163726020212, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 446.995, + "cuda_time_us": 788.5339999999999, + "pct_cuda_time": 2.2972097693831937, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 153.965, + "cuda_time_us": 484.506, + "pct_cuda_time": 1.4114951498917914, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 483.738, + "pct_cuda_time": 1.4092577611388826, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.832, + "cuda_time_us": 67.103, + "pct_cuda_time": 0.1954889290188127, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.103, + "pct_cuda_time": 0.1954889290188127, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.763, + "cuda_time_us": 236.925, + "pct_cuda_time": 0.69022569047259, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 236.125, + "pct_cuda_time": 0.6878950771883098, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2430.668, + "cuda_time_us": 1052.212, + "pct_cuda_time": 3.0653740813487174, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.15, + "cuda_time_us": 14.176, + "pct_cuda_time": 0.04129846739744407, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.176, + "pct_cuda_time": 0.04129846739744407, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1762.33, + "cuda_time_us": 236.159, + "pct_cuda_time": 0.6879941282528917, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 147.711, + "cuda_time_us": 107.35900000000001, + "pct_cuda_time": 0.31276538948378935, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.623, + "pct_cuda_time": 0.3106212252622516, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 542.132, + "cuda_time_us": 19.136, + "pct_cuda_time": 0.05574826975998093, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.136, + "pct_cuda_time": 0.05574826975998093, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 741.948, + "cuda_time_us": 29.729, + "pct_cuda_time": 0.08660850291045533, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.52, + "pct_cuda_time": 0.021907764872233306, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 20.864, + "pct_cuda_time": 0.06078239445402603, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.0039183435841959845, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 188.535, + "cuda_time_us": 79.935, + "pct_cuda_time": 0.23287196609866617, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 79.199, + "pct_cuda_time": 0.23072780187712844, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.511, + "cuda_time_us": 15.008, + "pct_cuda_time": 0.04372230521309541, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.008, + "pct_cuda_time": 0.04372230521309541, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 450.849, + "cuda_time_us": 786.8689999999999, + "pct_cuda_time": 2.2923591804852856, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.385, + "cuda_time_us": 483.962, + "pct_cuda_time": 1.4099103328584808, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 483.226, + "pct_cuda_time": 1.4077661686369431, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.097, + "cuda_time_us": 66.399, + "pct_cuda_time": 0.1934379893286462, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 66.399, + "pct_cuda_time": 0.1934379893286462, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 146.111, + "cuda_time_us": 236.50799999999998, + "pct_cuda_time": 0.6890108582981589, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.832, + "pct_cuda_time": 0.0024238378156513445, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 235.676, + "pct_cuda_time": 0.6865870204825075, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2425.899, + "cuda_time_us": 1062.6419999999998, + "pct_cuda_time": 3.095759452042519, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.738, + "cuda_time_us": 14.464, + "pct_cuda_time": 0.04213748817978492, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.464, + "pct_cuda_time": 0.04213748817978492, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1723.62, + "cuda_time_us": 241.53300000000002, + "pct_cuda_time": 0.7036500229900436, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 155.541, + "cuda_time_us": 107.934, + "pct_cuda_time": 0.31444051778186566, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 107.198, + "pct_cuda_time": 0.3122963535603279, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 510.498, + "cuda_time_us": 19.84, + "pct_cuda_time": 0.05779920945014745, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.84, + "pct_cuda_time": 0.05779920945014745, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 728.468, + "cuda_time_us": 30.656000000000002, + "pct_cuda_time": 0.08930910105361493, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.808, + "pct_cuda_time": 0.022746785654574157, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.376, + "pct_cuda_time": 0.062273986955965324, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004288328443075456, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 191.963, + "cuda_time_us": 83.10300000000001, + "pct_cuda_time": 0.24210119470441552, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 82.367, + "pct_cuda_time": 0.23995703048287778, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.148, + "cuda_time_us": 15.744, + "pct_cuda_time": 0.045866469434633135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.744, + "pct_cuda_time": 0.045866469434633135, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 482.048, + "cuda_time_us": 790.901, + "pct_cuda_time": 2.3041054714380578, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 160.437, + "cuda_time_us": 485.68899999999996, + "pct_cuda_time": 1.4149415442859206, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 484.953, + "pct_cuda_time": 1.412797380064383, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.598, + "cuda_time_us": 67.519, + "pct_cuda_time": 0.19670084792663842, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.519, + "pct_cuda_time": 0.19670084792663842, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 151.672, + "cuda_time_us": 237.69299999999998, + "pct_cuda_time": 0.6924630792254989, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.832, + "pct_cuda_time": 0.0024238378156513445, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 236.861, + "pct_cuda_time": 0.6900392414098475, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2641.146, + "cuda_time_us": 1060.049, + "pct_cuda_time": 3.0882053517348464, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.083, + "cuda_time_us": 14.208, + "pct_cuda_time": 0.04139169192881527, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.208, + "pct_cuda_time": 0.04139169192881527, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1947.477, + "cuda_time_us": 241.98000000000002, + "pct_cuda_time": 0.7049522531626351, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 150.148, + "cuda_time_us": 107.678, + "pct_cuda_time": 0.31369472153089606, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.942, + "pct_cuda_time": 0.31155055730935827, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 537.84, + "cuda_time_us": 20.064, + "pct_cuda_time": 0.05845178116974589, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 20.064, + "pct_cuda_time": 0.05845178116974589, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 917.571, + "cuda_time_us": 30.271000000000004, + "pct_cuda_time": 0.08818749341055514, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.551, + "pct_cuda_time": 0.021998076136999164, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.28, + "pct_cuda_time": 0.061994313361851704, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004195103911704251, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 196.885, + "cuda_time_us": 83.967, + "pct_cuda_time": 0.24461825705143803, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 83.199, + "pct_cuda_time": 0.24238086829852912, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 78.414, + "cuda_time_us": 15.296, + "pct_cuda_time": 0.04456132599543626, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.296, + "pct_cuda_time": 0.04456132599543626, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.888, + "cuda_time_us": 788.565, + "pct_cuda_time": 2.29730008064796, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 178.056, + "cuda_time_us": 485.433, + "pct_cuda_time": 1.414195748034951, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 484.697, + "pct_cuda_time": 1.4120515838134133, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.563, + "cuda_time_us": 67.615, + "pct_cuda_time": 0.19698052152075202, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.615, + "pct_cuda_time": 0.19698052152075202, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 147.044, + "cuda_time_us": 235.51700000000002, + "pct_cuda_time": 0.6861238110922571, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 234.717, + "pct_cuda_time": 0.6837931978079769, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2590.338, + "cuda_time_us": 1058.417, + "pct_cuda_time": 3.083450900634915, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 70.309, + "cuda_time_us": 14.4, + "pct_cuda_time": 0.0419510391170425, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.4, + "pct_cuda_time": 0.0419510391170425, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1885.034, + "cuda_time_us": 239.356, + "pct_cuda_time": 0.6973078415901962, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 174.419, + "cuda_time_us": 107.83800000000001, + "pct_cuda_time": 0.3141608441877521, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 107.102, + "pct_cuda_time": 0.31201667996621435, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 588.979, + "cuda_time_us": 19.52, + "pct_cuda_time": 0.0568669641364354, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.52, + "pct_cuda_time": 0.0568669641364354, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 757.117, + "cuda_time_us": 30.016000000000002, + "pct_cuda_time": 0.08744461042619084, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.552, + "pct_cuda_time": 0.022000989403604513, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.184, + "pct_cuda_time": 0.06171463976773809, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.003728981254848223, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 212.681, + "cuda_time_us": 81.982, + "pct_cuda_time": 0.23883542283981798, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 81.246, + "pct_cuda_time": 0.2366912586182802, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.278, + "cuda_time_us": 15.199, + "pct_cuda_time": 0.04427873913471729, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.199, + "pct_cuda_time": 0.04427873913471729, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 471.275, + "cuda_time_us": 789.462, + "pct_cuda_time": 2.2999132807929588, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 163.363, + "cuda_time_us": 485.498, + "pct_cuda_time": 1.4143851103642988, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 484.762, + "pct_cuda_time": 1.412240946142761, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.697, + "cuda_time_us": 67.583, + "pct_cuda_time": 0.19688729698938082, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.583, + "pct_cuda_time": 0.19688729698938082, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 155.464, + "cuda_time_us": 236.381, + "pct_cuda_time": 0.6886408734392795, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 235.581, + "pct_cuda_time": 0.6863102601549993, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2441.869, + "cuda_time_us": 1059.6370000000002, + "pct_cuda_time": 3.0870050858934426, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 91.692, + "cuda_time_us": 14.464, + "pct_cuda_time": 0.04213748817978492, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.464, + "pct_cuda_time": 0.04213748817978492, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1705.74, + "cuda_time_us": 238.334, + "pct_cuda_time": 0.6943304831195284, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 154.588, + "cuda_time_us": 107.104, + "pct_cuda_time": 0.31202250649942503, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.769, + "pct_cuda_time": 0.002240302019514284, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.335, + "pct_cuda_time": 0.3097822044799107, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 501.152, + "cuda_time_us": 19.296, + "pct_cuda_time": 0.05621439241683695, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.296, + "pct_cuda_time": 0.05621439241683695, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 720.461, + "cuda_time_us": 29.887, + "pct_cuda_time": 0.08706879903410066, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.584, + "pct_cuda_time": 0.02209421393497572, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 20.992, + "pct_cuda_time": 0.061155292579510856, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.003819292519614078, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 184.895, + "cuda_time_us": 82.047, + "pct_cuda_time": 0.23902478516916573, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 81.279, + "pct_cuda_time": 0.2367873964162568, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.254, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.04558679584051952, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.04558679584051952, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 487.085, + "cuda_time_us": 791.191, + "pct_cuda_time": 2.3049503187536096, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 154.154, + "cuda_time_us": 486.586, + "pct_cuda_time": 1.4175547444309198, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 485.85, + "pct_cuda_time": 1.415410580209382, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 126.205, + "cuda_time_us": 66.527, + "pct_cuda_time": 0.19381088745413103, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 66.527, + "pct_cuda_time": 0.19381088745413103, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 154.892, + "cuda_time_us": 238.078, + "pct_cuda_time": 0.6935846868685588, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 237.278, + "pct_cuda_time": 0.6912540735842786, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2510.153, + "cuda_time_us": 1056.9450000000002, + "pct_cuda_time": 3.0791625721918403, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 104.807, + "cuda_time_us": 14.688, + "pct_cuda_time": 0.04279005989938336, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.688, + "pct_cuda_time": 0.04279005989938336, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1788.386, + "cuda_time_us": 237.596, + "pct_cuda_time": 0.69218049236478, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 152.074, + "cuda_time_us": 107.743, + "pct_cuda_time": 0.3138840838602438, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.975, + "pct_cuda_time": 0.31164669510733484, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 487.299, + "cuda_time_us": 19.232, + "pct_cuda_time": 0.05602794335409454, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.232, + "pct_cuda_time": 0.05602794335409454, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 799.753, + "cuda_time_us": 30.495, + "pct_cuda_time": 0.08884006513015356, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.584, + "pct_cuda_time": 0.02209421393497572, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.407, + "pct_cuda_time": 0.06236429822073118, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004381552974446662, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 201.197, + "cuda_time_us": 80.126, + "pct_cuda_time": 0.23342840002028806, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.735, + "pct_cuda_time": 0.002141250954932378, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 79.391, + "pct_cuda_time": 0.23128714906535566, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.631, + "cuda_time_us": 15.615, + "pct_cuda_time": 0.045490658042542965, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.615, + "pct_cuda_time": 0.045490658042542965, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 460.778, + "cuda_time_us": 789.046, + "pct_cuda_time": 2.2987013618851337, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 157.745, + "cuda_time_us": 484.89, + "pct_cuda_time": 1.4126138442682459, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 484.154, + "pct_cuda_time": 1.4104696800467083, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.285, + "cuda_time_us": 67.167, + "pct_cuda_time": 0.19567537808155513, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.167, + "pct_cuda_time": 0.19567537808155513, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 150.852, + "cuda_time_us": 236.989, + "pct_cuda_time": 0.6904121395353324, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 236.189, + "pct_cuda_time": 0.6880815262510522, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2468.466, + "cuda_time_us": 1059.795, + "pct_cuda_time": 3.0874653820170876, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.657, + "cuda_time_us": 14.367, + "pct_cuda_time": 0.041854901319065954, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.367, + "pct_cuda_time": 0.041854901319065954, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1754.843, + "cuda_time_us": 241.279, + "pct_cuda_time": 0.7029100532722846, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 152.17, + "cuda_time_us": 107.711, + "pct_cuda_time": 0.3137908593288726, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.975, + "pct_cuda_time": 0.31164669510733484, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 530.745, + "cuda_time_us": 19.424, + "pct_cuda_time": 0.056587290542321776, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 19.424, + "pct_cuda_time": 0.056587290542321776, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 743.739, + "cuda_time_us": 30.272000000000002, + "pct_cuda_time": 0.08819040667716047, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.648, + "pct_cuda_time": 0.02228066299771813, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.12, + "pct_cuda_time": 0.06152819070499568, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.004381552974446662, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 186.325, + "cuda_time_us": 83.872, + "pct_cuda_time": 0.2443414967239298, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0021470774881430783, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 83.135, + "pct_cuda_time": 0.24219441923578672, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.526, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.045493571309148315, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.045493571309148315, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 482.912, + "cuda_time_us": 788.533, + "pct_cuda_time": 2.297206856116589, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 155.413, + "cuda_time_us": 484.85699999999997, + "pct_cuda_time": 1.4125177064702692, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 484.121, + "pct_cuda_time": 1.4103735422487316, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.171, + "cuda_time_us": 67.104, + "pct_cuda_time": 0.19549184228541808, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.104, + "pct_cuda_time": 0.19549184228541808, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 152.837, + "cuda_time_us": 236.572, + "pct_cuda_time": 0.6891973073609013, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.864, + "pct_cuda_time": 0.0025170623470225505, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 235.708, + "pct_cuda_time": 0.6866802450138788, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2329.091, + "cuda_time_us": 1056.469, + "pct_cuda_time": 3.077775857287693, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.111, + "cuda_time_us": 14.464, + "pct_cuda_time": 0.04213748817978492, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.464, + "pct_cuda_time": 0.04213748817978492, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1659.008, + "cuda_time_us": 237.47, + "pct_cuda_time": 0.6918134207725057, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.692, + "cuda_time_us": 107.551, + "pct_cuda_time": 0.31332473667201655, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 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": 106.783, + "pct_cuda_time": 0.31108734791910764, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[1536, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 495.444, + "cuda_time_us": 20.128, + "pct_cuda_time": 0.0586382302324883, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 20.128, + "pct_cuda_time": 0.0586382302324883, + "trace": "_C::rotary_embedding(int64[1536], bfloat16[1536, 4096], bfloat16[1536, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 697.2, + "cuda_time_us": 30.047, + "pct_cuda_time": 0.08753492169095668, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 7.744, + "pct_cuda_time": 0.022560336591831747, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[1536], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, false, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 21.023, + "pct_cuda_time": 0.0612456038442767, + "trace": "_vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_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": 0.003728981254848223, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], None, None, bfloat16[1536, 32, 128], int32[13], int32[13], None, None, None, 128, 128, None, None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[1536, 32, 128], bfloat16[1536, 8, 128], bfloat16[1536, 8, 128], bfloat16[1536, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 178.035, + "cuda_time_us": 79.744, + "pct_cuda_time": 0.23231553217704426, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0021470774881430783, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 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": 79.007, + "pct_cuda_time": 0.2301684546889012, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[1536, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.318, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.04558679584051952, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.04558679584051952, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 439.726, + "cuda_time_us": 788.8870000000001, + "pct_cuda_time": 2.298238152494883, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 152.636, + "cuda_time_us": 484.63500000000005, + "pct_cuda_time": 1.4118709612838818, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.0021470774881430783, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 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": 483.898, + "pct_cuda_time": 1.4097238837957387, + "trace": "mm(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[1536, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[1536, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 92.039, + "cuda_time_us": 67.391, + "pct_cuda_time": 0.19632794980115356, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 67.391, + "pct_cuda_time": 0.19632794980115356, + "trace": "_C::silu_and_mul(bfloat16[1536, 14336], bfloat16[1536, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.214, + "cuda_time_us": 236.86100000000002, + "pct_cuda_time": 0.6900392414098476, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_on_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 236.061, + "pct_cuda_time": 0.6877086281255674, + "trace": "mm(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[1536, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[1536, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.578, + "cuda_time_us": 14.559, + "pct_cuda_time": 0.04241424850729318, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 14.559, + "pct_cuda_time": 0.04241424850729318, + "trace": "_C::fused_add_rms_norm(bfloat16[1536, 4096], bfloat16[1536, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 522.073, + "cuda_time_us": 363.9, + "pct_cuda_time": 1.0601377176869282, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 7.233, + "pct_cuda_time": 0.021071657356497808, + "trace": "index_select(bfloat16[1536, 4096], 0, int64[12])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[12, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 355.931, + "pct_cuda_time": 1.0369218961088926, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[12, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 3939.941, + "cuda_time_us": 127.26100000000001, + "pct_cuda_time": 0.37074522146346856, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.002144164221537728, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "copy_(int32[12], int32[12], True) <- _to_copy(int32[12], 3, 0, None, None, True, None) <- to(int32[12], 3, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.768, + "pct_cuda_time": 0.0022373887529089334, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.0023306132842801394, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 7.008, + "pct_cuda_time": 0.02041617237029402, + "trace": "copy_(float32[12, 128256], bfloat16[12, 128256], False) <- _to_copy(bfloat16[12, 128256], 6, None, None, None, False, None) <- to(bfloat16[12, 128256], 6, False, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::elementwise_kernel<128, 4, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1})", + "cpu_time_us": 0, + "cuda_time_us": 8.544, + "pct_cuda_time": 0.02489094987611189, + "trace": "div_(float32[12, 128256], bfloat16[12, 1])" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::SoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 35.743, + "pct_cuda_time": 0.10412888827503128, + "trace": "_softmax(float32[12, 128256], -1, False) <- softmax(float32[12, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::LogSoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 28.831, + "pct_cuda_time": 0.08399238949885086, + "trace": "_log_softmax(float32[12, 128256], -1, False) <- log_softmax(float32[12, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 2.048, + "pct_cuda_time": 0.005966370007757156, + "trace": "copy_(int64[12], int32[12], False) <- _to_copy(int32[12], 4, None, None, None, False, None) <- to(int32[12], 4, False, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::index_elementwise_kernel<128, 4, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1}>(long, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1})", + "cpu_time_us": 0, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.026755440503535993, + "trace": "index(float32[12, 128256], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, long, 4> >(at::native::ReduceOp, unsigned int, long, 4>)", + "cpu_time_us": 0, + "cuda_time_us": 27.744, + "pct_cuda_time": 0.08082566869883523, + "trace": "argmax(float32[12, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 2.783, + "pct_cuda_time": 0.008107620962689535, + "trace": "copy_(int64[12], int64[12], False) <- _to_copy(int64[12], 4, 0, None, None, False, None) <- to(int64[12], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + }, + "decode_1": { + "metadata": { + "num_running_seqs": 12 + }, + "summary_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cuda_time_us": 6306.274000000001, + "pct_cuda_time": 92.97781161620733, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cuda_time_us": 9.376, + "pct_cuda_time": 0.13823693066834075, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cuda_time_us": 9.376, + "pct_cuda_time": 0.13823693066834075, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cuda_time_us": 6293.825000000001, + "pct_cuda_time": 92.79426729561324, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cuda_time_us": 204.31100000000004, + "pct_cuda_time": 3.012300079114694, + "invocations": 64 + }, + "children": [ + { + "entry": { + "name": "void vllm::rms_norm_kernel(c10::BFloat16*, c10::BFloat16 const*, c10::BFloat16 const*, float, int, int)", + "cuda_time_us": 4.385, + "pct_cuda_time": 0.06465112425135176, + "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": 199.92600000000002, + "pct_cuda_time": 2.9476489548633418, + "invocations": 63 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cuda_time_us": 1790.2779999999998, + "pct_cuda_time": 26.395321647083588, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cuda_time_us": 684.5010000000001, + "pct_cuda_time": 10.09207735488587, + "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": 684.5010000000001, + "pct_cuda_time": 10.09207735488587, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cuda_time_us": 117.856, + "pct_cuda_time": 1.7376335005170613, + "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": 117.856, + "pct_cuda_time": 1.7376335005170613, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cuda_time_us": 483.0649999999999, + "pct_cuda_time": 7.122165413108148, + "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": 79.04, + "pct_cuda_time": 1.1653420435180943, + "invocations": 32 + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cuda_time_us": 359.322, + "pct_cuda_time": 5.2977357510249075, + "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": 44.703, + "pct_cuda_time": 0.659087618565149, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cuda_time_us": 504.856, + "pct_cuda_time": 7.443445378572508, + "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": 504.856, + "pct_cuda_time": 7.443445378572508, + "invocations": 32 + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cuda_time_us": 4299.236000000001, + "pct_cuda_time": 63.386645569414966, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cuda_time_us": 2604.343999999999, + "pct_cuda_time": 38.397666485122556, + "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": 2604.343999999999, + "pct_cuda_time": 38.397666485122556, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cuda_time_us": 290.17300000000006, + "pct_cuda_time": 4.278223643645952, + "invocations": 32 + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cuda_time_us": 290.17300000000006, + "pct_cuda_time": 4.278223643645952, + "invocations": 32 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cuda_time_us": 1404.7190000000003, + "pct_cuda_time": 20.710755440646437, + "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": 1404.7190000000003, + "pct_cuda_time": 20.710755440646437, + "invocations": 32 + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cuda_time_us": 3.073, + "pct_cuda_time": 0.04530738992574777, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cuda_time_us": 3.073, + "pct_cuda_time": 0.04530738992574777, + "invocations": 1 + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cuda_time_us": 351.38800000000003, + "pct_cuda_time": 5.1807592356748, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cuda_time_us": 7.231, + "pct_cuda_time": 0.1066116942899714, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cuda_time_us": 0.737, + "pct_cuda_time": 0.010866106858208952, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cuda_time_us": 343.42, + "pct_cuda_time": 5.063281434526619, + "invocations": 1 + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cuda_time_us": 124.896, + "pct_cuda_time": 1.841429148117863, + "invocations": 1 + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cuda_time_us": 5.441, + "pct_cuda_time": 0.08022047139147205, + "invocations": 7 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cuda_time_us": 6.784, + "pct_cuda_time": 0.10002126041531822, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::elementwise_kernel<128, 4, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1})", + "cuda_time_us": 8.928, + "pct_cuda_time": 0.13163175309374428, + "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": 34.816, + "pct_cuda_time": 0.5133166572257841, + "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": 28.192, + "pct_cuda_time": 0.41565438880139316, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cuda_time_us": 1.824, + "pct_cuda_time": 0.026892508696571407, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::index_elementwise_kernel<128, 4, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1}>(long, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1})", + "cuda_time_us": 9.056, + "pct_cuda_time": 0.13351894668648612, + "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": 27.295, + "pct_cuda_time": 0.4024292899522569, + "invocations": 1 + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cuda_time_us": 2.56, + "pct_cuda_time": 0.03774387185483707, + "invocations": 1 + }, + "children": [] + } + ] + } + ], + "model_stats": [ + { + "entry": { + "name": "LlamaForCausalLM", + "cpu_time_us": 79570.592, + "cuda_time_us": 6306.274000000001, + "pct_cuda_time": 92.97781161620733, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "VocabParallelEmbedding(weight=bfloat16[128256, 4096])", + "cpu_time_us": 299.024, + "cuda_time_us": 9.376, + "pct_cuda_time": 0.13823693066834075, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 9.376, + "pct_cuda_time": 0.13823693066834075, + "trace": "index_select(bfloat16[128256, 4096], 0, int64[12]) <- embedding(bfloat16[128256, 4096], int64[12], -1, False, False)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 4470.946, + "cuda_time_us": 204.766, + "pct_cuda_time": 3.019008462588893, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 271.977, + "cuda_time_us": 4.385, + "pct_cuda_time": 0.06465112425135176, + "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": 4.385, + "pct_cuda_time": 0.06465112425135176, + "trace": "_C::rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 3306.463, + "cuda_time_us": 62.494, + "pct_cuda_time": 0.9213927842563233, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 668.393, + "cuda_time_us": 26.719, + "pct_cuda_time": 0.39393691878491854, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 26.719, + "pct_cuda_time": 0.39393691878491854, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 1030.463, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.05472861418951375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05472861418951375, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 1050.218, + "cuda_time_us": 15.392, + "pct_cuda_time": 0.22693502952720784, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.304, + "pct_cuda_time": 0.03396948466935336, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.808, + "pct_cuda_time": 0.17409360893043596, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.018871935927418534, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 286.509, + "cuda_time_us": 16.671, + "pct_cuda_time": 0.24579222175468307, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.671, + "pct_cuda_time": 0.24579222175468307, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 124.254, + "cuda_time_us": 3.36, + "pct_cuda_time": 0.049538831809473646, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.36, + "pct_cuda_time": 0.049538831809473646, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 613.318, + "cuda_time_us": 134.527, + "pct_cuda_time": 1.9834257222717442, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 209.794, + "cuda_time_us": 81.567, + "pct_cuda_time": 1.2025993732748026, + "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": 1.2025993732748026, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 148.003, + "cuda_time_us": 9.184, + "pct_cuda_time": 0.13540614027922795, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13540614027922795, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 186.459, + "cuda_time_us": 43.776, + "pct_cuda_time": 0.6454202087177139, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.776, + "pct_cuda_time": 0.6454202087177139, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2496.878, + "cuda_time_us": 197.564, + "pct_cuda_time": 2.912824335597277, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.771, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.047179839818546336, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047179839818546336, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1794.175, + "cuda_time_us": 56.863, + "pct_cuda_time": 0.8383710098756251, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 144.507, + "cuda_time_us": 21.887, + "pct_cuda_time": 0.3226953606589136, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.887, + "pct_cuda_time": 0.3226953606589136, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 540.679, + "cuda_time_us": 3.585, + "pct_cuda_time": 0.052856164296715184, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.585, + "pct_cuda_time": 0.052856164296715184, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 758.747, + "cuda_time_us": 15.711999999999998, + "pct_cuda_time": 0.23165301350906248, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.88, + "pct_cuda_time": 0.042461855836691695, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.328, + "pct_cuda_time": 0.16701663295765398, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.022174524714716776, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 189.483, + "cuda_time_us": 15.679, + "pct_cuda_time": 0.23116647141093374, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.679, + "pct_cuda_time": 0.23116647141093374, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.247, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04670804142036087, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04670804142036087, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 448.118, + "cuda_time_us": 134.333, + "pct_cuda_time": 1.9805654444827452, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 143.931, + "cuda_time_us": 80.766, + "pct_cuda_time": 1.190789669620223, + "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": 80.766, + "pct_cuda_time": 1.190789669620223, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 104.169, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.13210355149192973, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13210355149192973, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.41, + "cuda_time_us": 44.607, + "pct_cuda_time": 0.6576722233705925, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.607, + "pct_cuda_time": 0.6576722233705925, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2434.801, + "cuda_time_us": 196.605, + "pct_cuda_time": 2.8986851273516563, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.294, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04670804142036087, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04670804142036087, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1734.02, + "cuda_time_us": 55.358999999999995, + "pct_cuda_time": 0.816196485160908, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 138.1, + "cuda_time_us": 20.735, + "pct_cuda_time": 0.3057106183242369, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.735, + "pct_cuda_time": 0.3057106183242369, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 523.278, + "cuda_time_us": 3.679, + "pct_cuda_time": 0.054242072091384985, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.054242072091384985, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 742.914, + "cuda_time_us": 15.072999999999999, + "pct_cuda_time": 0.2222317892452965, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.401, + "pct_cuda_time": 0.03539962356385304, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.168, + "pct_cuda_time": 0.16465764096672666, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.022174524714716776, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 179.993, + "cuda_time_us": 15.872, + "pct_cuda_time": 0.2340120054999898, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.872, + "pct_cuda_time": 0.2340120054999898, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.938, + "cuda_time_us": 3.071, + "pct_cuda_time": 0.04527790252586118, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04527790252586118, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 439.41, + "cuda_time_us": 135.007, + "pct_cuda_time": 1.9905026982445264, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 142.445, + "cuda_time_us": 81.311, + "pct_cuda_time": 1.1988249860893192, + "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": 1.1988249860893192, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.251, + "cuda_time_us": 9.376, + "pct_cuda_time": 0.13823693066834075, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.376, + "pct_cuda_time": 0.13823693066834075, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 143.8, + "cuda_time_us": 44.32, + "pct_cuda_time": 0.6534407814868667, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.32, + "pct_cuda_time": 0.6534407814868667, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2308.457, + "cuda_time_us": 195.004, + "pct_cuda_time": 2.87508046374244, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.778, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04529264622580448, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04529264622580448, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1660.254, + "cuda_time_us": 54.847, + "pct_cuda_time": 0.8086477107899408, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 134.129, + "cuda_time_us": 20.767, + "pct_cuda_time": 0.3061824167224224, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.767, + "pct_cuda_time": 0.3061824167224224, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 506.899, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05331321899495735, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05331321899495735, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 706.666, + "cuda_time_us": 14.655999999999999, + "pct_cuda_time": 0.21608366636894216, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.368, + "pct_cuda_time": 0.034913081465724284, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.008, + "pct_cuda_time": 0.16229864897579935, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.018871935927418534, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 172.664, + "cuda_time_us": 15.808, + "pct_cuda_time": 0.23306840870361886, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.808, + "pct_cuda_time": 0.23306840870361886, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.761, + "cuda_time_us": 3.135, + "pct_cuda_time": 0.0462214993222321, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0462214993222321, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 420.974, + "cuda_time_us": 133.95, + "pct_cuda_time": 1.9749186074044627, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 138.438, + "cuda_time_us": 81.791, + "pct_cuda_time": 1.2059019620621008, + "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": 1.2059019620621008, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.221, + "cuda_time_us": 9.088, + "pct_cuda_time": 0.13399074508467157, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.088, + "pct_cuda_time": 0.13399074508467157, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 134.87, + "cuda_time_us": 43.071, + "pct_cuda_time": 0.6350259002576902, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.071, + "pct_cuda_time": 0.6350259002576902, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2386.649, + "cuda_time_us": 196.47600000000003, + "pct_cuda_time": 2.8967831900589722, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.304, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.047179839818546336, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047179839818546336, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1700.229, + "cuda_time_us": 56.03, + "pct_cuda_time": 0.8260895078228596, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 132.397, + "cuda_time_us": 21.759, + "pct_cuda_time": 0.3208081670661717, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.3208081670661717, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 511.234, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.05472861418951375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05472861418951375, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 722.138, + "cuda_time_us": 15.072, + "pct_cuda_time": 0.2222170455453532, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036800275058466135, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.264, + "pct_cuda_time": 0.16607303616128305, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.019343734325603996, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 190.123, + "cuda_time_us": 15.487, + "pct_cuda_time": 0.22833568102182092, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.487, + "pct_cuda_time": 0.22833568102182092, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.521, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04623624302217541, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04623624302217541, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 453.546, + "cuda_time_us": 134.11, + "pct_cuda_time": 1.9772775993953902, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 146.424, + "cuda_time_us": 81.695, + "pct_cuda_time": 1.2044865668675444, + "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": 1.2044865668675444, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.681, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.1325753498901152, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1325753498901152, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.756, + "cuda_time_us": 43.423, + "pct_cuda_time": 0.6402156826377304, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.423, + "pct_cuda_time": 0.6402156826377304, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2461.139, + "cuda_time_us": 195.57999999999998, + "pct_cuda_time": 2.8835728349097782, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.741, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.047651638216731795, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047651638216731795, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1763.291, + "cuda_time_us": 55.422, + "pct_cuda_time": 0.8171253382573358, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 136.248, + "cuda_time_us": 20.575, + "pct_cuda_time": 0.3033516263333096, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.575, + "pct_cuda_time": 0.3033516263333096, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 485.51, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.05472861418951375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05472861418951375, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 804.226, + "cuda_time_us": 15.167, + "pct_cuda_time": 0.22361769703996628, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036328476660280676, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.231, + "pct_cuda_time": 0.16558649406315434, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.472, + "pct_cuda_time": 0.02170272631653131, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 192.633, + "cuda_time_us": 15.968, + "pct_cuda_time": 0.2354274006945462, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.2354274006945462, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 82.512, + "cuda_time_us": 3.296, + "pct_cuda_time": 0.048595235013102714, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.048595235013102714, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 460.807, + "cuda_time_us": 133.63, + "pct_cuda_time": 1.9702006234226082, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 172.759, + "cuda_time_us": 81.246, + "pct_cuda_time": 1.1978666455930047, + "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": 81.246, + "pct_cuda_time": 1.1978666455930047, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.783, + "cuda_time_us": 8.896, + "pct_cuda_time": 0.1311599546955588, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1311599546955588, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 137.832, + "cuda_time_us": 43.488, + "pct_cuda_time": 0.6411740231340446, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6411740231340446, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2265.587, + "cuda_time_us": 196.512, + "pct_cuda_time": 2.8973139632569302, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.325, + "cuda_time_us": 3.296, + "pct_cuda_time": 0.048595235013102714, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.048595235013102714, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1607.113, + "cuda_time_us": 56.289, + "pct_cuda_time": 0.8299081261081732, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 134.753, + "cuda_time_us": 21.6, + "pct_cuda_time": 0.31846391877518776, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.6, + "pct_cuda_time": 0.31846391877518776, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 480.194, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05331321899495735, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05331321899495735, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 676.672, + "cuda_time_us": 15.073, + "pct_cuda_time": 0.2222317892452965, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.4, + "pct_cuda_time": 0.03538487986390975, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.201, + "pct_cuda_time": 0.16514418306485545, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.472, + "pct_cuda_time": 0.02170272631653131, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 177.024, + "cuda_time_us": 16.0, + "pct_cuda_time": 0.23589919909273163, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 16.0, + "pct_cuda_time": 0.23589919909273163, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.144, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04670804142036087, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04670804142036087, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 429.039, + "cuda_time_us": 133.75900000000001, + "pct_cuda_time": 1.9721025607152936, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 141.378, + "cuda_time_us": 81.343, + "pct_cuda_time": 1.1992967844875044, + "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": 1.1992967844875044, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.078, + "cuda_time_us": 8.864, + "pct_cuda_time": 0.13068815629737335, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13068815629737335, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 138.562, + "cuda_time_us": 43.552, + "pct_cuda_time": 0.6421176199304155, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.552, + "pct_cuda_time": 0.6421176199304155, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2350.854, + "cuda_time_us": 196.095, + "pct_cuda_time": 2.891165840380576, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.79, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04670804142036087, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04670804142036087, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1680.112, + "cuda_time_us": 55.681, + "pct_cuda_time": 0.8209439565426494, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.73, + "cuda_time_us": 20.897, + "pct_cuda_time": 0.3080990977150508, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.897, + "pct_cuda_time": 0.3080990977150508, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 521.677, + "cuda_time_us": 4.032, + "pct_cuda_time": 0.059446598171368374, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 4.032, + "pct_cuda_time": 0.059446598171368374, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 707.291, + "cuda_time_us": 14.879999999999999, + "pct_cuda_time": 0.2193862551562404, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.4, + "pct_cuda_time": 0.03538487986390975, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.2, + "pct_cuda_time": 0.16512943936491215, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.018871935927418534, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 169.887, + "cuda_time_us": 15.872, + "pct_cuda_time": 0.2340120054999898, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.872, + "pct_cuda_time": 0.2340120054999898, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.008, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.047179839818546336, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047179839818546336, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 434.184, + "cuda_time_us": 134.046, + "pct_cuda_time": 1.976334002599019, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 137.239, + "cuda_time_us": 81.439, + "pct_cuda_time": 1.2007121796820608, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.439, + "pct_cuda_time": 1.2007121796820608, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.402, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.13446254348285702, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13446254348285702, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 148.532, + "cuda_time_us": 43.487, + "pct_cuda_time": 0.6411592794341013, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6411592794341013, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2229.308, + "cuda_time_us": 197.372, + "pct_cuda_time": 2.909993545208165, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.131, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.045764444623989944, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045764444623989944, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1591.53, + "cuda_time_us": 56.223, + "pct_cuda_time": 0.8289350419119157, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 133.735, + "cuda_time_us": 21.663, + "pct_cuda_time": 0.31939277187161536, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.663, + "pct_cuda_time": 0.31939277187161536, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 482.978, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.055672210985884665, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.055672210985884665, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 672.217, + "cuda_time_us": 15.072, + "pct_cuda_time": 0.2222170455453532, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.0372720734566516, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.264, + "pct_cuda_time": 0.16607303616128305, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.018871935927418534, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 165.521, + "cuda_time_us": 15.712, + "pct_cuda_time": 0.23165301350906248, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.712, + "pct_cuda_time": 0.23165301350906248, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.218, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04670804142036087, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04670804142036087, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 428.334, + "cuda_time_us": 134.877, + "pct_cuda_time": 1.988586017251898, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 147.456, + "cuda_time_us": 81.246, + "pct_cuda_time": 1.1978666455930047, + "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": 81.246, + "pct_cuda_time": 1.1978666455930047, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.616, + "cuda_time_us": 9.152, + "pct_cuda_time": 0.1349343418810425, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1349343418810425, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 134.565, + "cuda_time_us": 44.479, + "pct_cuda_time": 0.6557850297778507, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6557850297778507, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2522.486, + "cuda_time_us": 196.38, + "pct_cuda_time": 2.895367794864415, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.372, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04623624302217541, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04623624302217541, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1851.752, + "cuda_time_us": 55.486999999999995, + "pct_cuda_time": 0.81808367875365, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 259.103, + "cuda_time_us": 20.767, + "pct_cuda_time": 0.3061824167224224, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.767, + "pct_cuda_time": 0.3061824167224224, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 543.472, + "cuda_time_us": 3.52, + "pct_cuda_time": 0.05189782380040096, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.52, + "pct_cuda_time": 0.05189782380040096, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 721.93, + "cuda_time_us": 15.296, + "pct_cuda_time": 0.22551963433265146, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.656, + "pct_cuda_time": 0.03915926704939345, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.168, + "pct_cuda_time": 0.16465764096672666, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.472, + "pct_cuda_time": 0.02170272631653131, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 179.654, + "cuda_time_us": 15.904, + "pct_cuda_time": 0.23448380389817525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.904, + "pct_cuda_time": 0.23448380389817525, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 84.082, + "cuda_time_us": 3.135, + "pct_cuda_time": 0.0462214993222321, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0462214993222321, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 447.255, + "cuda_time_us": 134.622, + "pct_cuda_time": 1.9848263737663578, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 150.555, + "cuda_time_us": 81.599, + "pct_cuda_time": 1.203071171672988, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.599, + "pct_cuda_time": 1.203071171672988, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 99.1, + "cuda_time_us": 9.408, + "pct_cuda_time": 0.1387087290665262, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1387087290665262, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 140.745, + "cuda_time_us": 43.615, + "pct_cuda_time": 0.6430464730268431, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.615, + "pct_cuda_time": 0.6430464730268431, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2259.027, + "cuda_time_us": 197.34, + "pct_cuda_time": 2.909521746809979, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.988, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.047179839818546336, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047179839818546336, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1591.168, + "cuda_time_us": 56.255, + "pct_cuda_time": 0.8294068403101013, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 141.344, + "cuda_time_us": 21.632, + "pct_cuda_time": 0.3189357171733732, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.3189357171733732, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 466.175, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053785017393142814, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.053785017393142814, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 684.193, + "cuda_time_us": 14.847000000000001, + "pct_cuda_time": 0.21889971305811168, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03585667826209521, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 10.912, + "pct_cuda_time": 0.160883253781243, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.503, + "pct_cuda_time": 0.022159781014773478, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 163.239, + "cuda_time_us": 16.128, + "pct_cuda_time": 0.2377863926854735, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.2377863926854735, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.942, + "cuda_time_us": 3.071, + "pct_cuda_time": 0.04527790252586118, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04527790252586118, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 443.712, + "cuda_time_us": 134.814, + "pct_cuda_time": 1.9876571641554701, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 135.741, + "cuda_time_us": 81.823, + "pct_cuda_time": 1.2063737604602862, + "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": 81.823, + "pct_cuda_time": 1.2063737604602862, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 119.465, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.1325753498901152, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1325753498901152, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.087, + "cuda_time_us": 43.999, + "pct_cuda_time": 0.6487080538050688, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6487080538050688, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2430.766, + "cuda_time_us": 194.813, + "pct_cuda_time": 2.8722644170532705, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 74.01, + "cuda_time_us": 3.039, + "pct_cuda_time": 0.044806104127675724, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.039, + "pct_cuda_time": 0.044806104127675724, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1778.664, + "cuda_time_us": 55.295, + "pct_cuda_time": 0.8152528883645374, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 142.53, + "cuda_time_us": 20.608, + "pct_cuda_time": 0.30383816843143835, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.608, + "pct_cuda_time": 0.30383816843143835, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 493.638, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053785017393142814, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.053785017393142814, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 723.672, + "cuda_time_us": 14.879, + "pct_cuda_time": 0.21937151145629713, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.4, + "pct_cuda_time": 0.03538487986390975, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.199, + "pct_cuda_time": 0.16511469566496886, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.018871935927418534, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 179.069, + "cuda_time_us": 16.16, + "pct_cuda_time": 0.23825819108365898, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.23825819108365898, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.334, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.045764444623989944, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045764444623989944, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 423.708, + "cuda_time_us": 133.375, + "pct_cuda_time": 1.966440979937068, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 137.085, + "cuda_time_us": 80.991, + "pct_cuda_time": 1.1941070021074645, + "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": 1.1941070021074645, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.028, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.13210355149192973, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13210355149192973, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 137.26, + "cuda_time_us": 43.424, + "pct_cuda_time": 0.6402304263376737, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.424, + "pct_cuda_time": 0.6402304263376737, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2520.604, + "cuda_time_us": 196.957, + "pct_cuda_time": 2.9038749097316967, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.006, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.047651638216731795, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047651638216731795, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1868.239, + "cuda_time_us": 56.287, + "pct_cuda_time": 0.8298786387082866, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 132.651, + "cuda_time_us": 21.824, + "pct_cuda_time": 0.321766507562486, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.824, + "pct_cuda_time": 0.321766507562486, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 472.183, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05614400938407013, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05614400938407013, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 941.1, + "cuda_time_us": 15.039, + "pct_cuda_time": 0.22173050344722448, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036800275058466135, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.231, + "pct_cuda_time": 0.16558649406315434, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.019343734325603996, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 184.977, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.2302376183145061, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.2302376183145061, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.414, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04670804142036087, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04670804142036087, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 430.679, + "cuda_time_us": 134.27, + "pct_cuda_time": 1.9796365913863176, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 141.363, + "cuda_time_us": 80.863, + "pct_cuda_time": 1.1922198085147224, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 80.863, + "pct_cuda_time": 1.1922198085147224, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 102.037, + "cuda_time_us": 8.768, + "pct_cuda_time": 0.12927276110281694, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.768, + "pct_cuda_time": 0.12927276110281694, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 134.348, + "cuda_time_us": 44.639, + "pct_cuda_time": 0.6581440217687781, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6581440217687781, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2189.48, + "cuda_time_us": 196.50900000000001, + "pct_cuda_time": 2.8972697321571004, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.032, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.047179839818546336, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047179839818546336, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1566.33, + "cuda_time_us": 55.295, + "pct_cuda_time": 0.8152528883645374, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 131.371, + "cuda_time_us": 20.768, + "pct_cuda_time": 0.30619716042236567, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.768, + "pct_cuda_time": 0.30619716042236567, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 458.695, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.055672210985884665, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.055672210985884665, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 683.961, + "cuda_time_us": 15.2, + "pct_cuda_time": 0.22410423913809507, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036800275058466135, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.232, + "pct_cuda_time": 0.1656012377630976, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.472, + "pct_cuda_time": 0.02170272631653131, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 159.164, + "cuda_time_us": 15.551, + "pct_cuda_time": 0.22927927781819188, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.551, + "pct_cuda_time": 0.22927927781819188, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.682, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.047179839818546336, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047179839818546336, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 414.307, + "cuda_time_us": 134.814, + "pct_cuda_time": 1.9876571641554701, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 135.566, + "cuda_time_us": 81.887, + "pct_cuda_time": 1.2073173572566573, + "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": 81.887, + "pct_cuda_time": 1.2073173572566573, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.248, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.13351894668648612, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13351894668648612, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 135.06, + "cuda_time_us": 43.871, + "pct_cuda_time": 0.6468208602123269, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.871, + "pct_cuda_time": 0.6468208602123269, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2404.876, + "cuda_time_us": 197.406, + "pct_cuda_time": 2.9104948310062366, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 66.543, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.047651638216731795, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047651638216731795, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1736.111, + "cuda_time_us": 56.127, + "pct_cuda_time": 0.8275196467173593, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 166.375, + "cuda_time_us": 21.343, + "pct_cuda_time": 0.3146747878897607, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.343, + "pct_cuda_time": 0.3146747878897607, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 517.69, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05331321899495735, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05331321899495735, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 730.099, + "cuda_time_us": 15.104, + "pct_cuda_time": 0.22268884394353866, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03585667826209521, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.232, + "pct_cuda_time": 0.1656012377630976, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.44, + "pct_cuda_time": 0.021230927918345847, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 192.063, + "cuda_time_us": 16.064, + "pct_cuda_time": 0.2368427958891026, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.2368427958891026, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 87.466, + "cuda_time_us": 3.328, + "pct_cuda_time": 0.04906703341128818, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.328, + "pct_cuda_time": 0.04906703341128818, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 442.074, + "cuda_time_us": 134.719, + "pct_cuda_time": 1.986256512660857, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 144.513, + "cuda_time_us": 81.567, + "pct_cuda_time": 1.2025993732748026, + "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": 1.2025993732748026, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 101.256, + "cuda_time_us": 9.44, + "pct_cuda_time": 0.13918052746471168, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13918052746471168, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 139.302, + "cuda_time_us": 43.712, + "pct_cuda_time": 0.644476611921343, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.712, + "pct_cuda_time": 0.644476611921343, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2262.544, + "cuda_time_us": 195.517, + "pct_cuda_time": 2.882643981813351, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.967, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04529264622580448, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04529264622580448, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1591.47, + "cuda_time_us": 55.104, + "pct_cuda_time": 0.8124368416753678, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 138.078, + "cuda_time_us": 20.608, + "pct_cuda_time": 0.30383816843143835, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.608, + "pct_cuda_time": 0.30383816843143835, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 464.717, + "cuda_time_us": 3.808, + "pct_cuda_time": 0.05614400938407013, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05614400938407013, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 680.95, + "cuda_time_us": 14.943999999999999, + "pct_cuda_time": 0.22032985195261134, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036800275058466135, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.136, + "pct_cuda_time": 0.1641858425685412, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.019343734325603996, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 166.991, + "cuda_time_us": 15.744, + "pct_cuda_time": 0.23212481190724796, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.744, + "pct_cuda_time": 0.23212481190724796, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.867, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.045764444623989944, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045764444623989944, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 448.802, + "cuda_time_us": 134.237, + "pct_cuda_time": 1.9791500492881884, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 138.518, + "cuda_time_us": 81.31, + "pct_cuda_time": 1.1988102423893758, + "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": 81.31, + "pct_cuda_time": 1.1988102423893758, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 116.703, + "cuda_time_us": 9.28, + "pct_cuda_time": 0.13682153547378434, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13682153547378434, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.417, + "cuda_time_us": 43.647, + "pct_cuda_time": 0.6435182714250286, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.647, + "pct_cuda_time": 0.6435182714250286, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2240.725, + "cuda_time_us": 196.799, + "pct_cuda_time": 2.9015454051406557, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.218, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.047651638216731795, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047651638216731795, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1605.082, + "cuda_time_us": 56.352000000000004, + "pct_cuda_time": 0.830836979204601, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 133.378, + "cuda_time_us": 21.504, + "pct_cuda_time": 0.31704852358063135, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.31704852358063135, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 475.192, + "cuda_time_us": 3.776, + "pct_cuda_time": 0.055672210985884665, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.055672210985884665, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 687.953, + "cuda_time_us": 15.168, + "pct_cuda_time": 0.2236324407399096, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.0372720734566516, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.328, + "pct_cuda_time": 0.16701663295765398, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.019343734325603996, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 175.05, + "cuda_time_us": 15.904, + "pct_cuda_time": 0.23448380389817525, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.904, + "pct_cuda_time": 0.23448380389817525, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.959, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04623624302217541, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04623624302217541, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 420.004, + "cuda_time_us": 134.079, + "pct_cuda_time": 1.976820544697148, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 139.859, + "cuda_time_us": 81.663, + "pct_cuda_time": 1.204014768469359, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.663, + "pct_cuda_time": 1.204014768469359, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 95.247, + "cuda_time_us": 8.992, + "pct_cuda_time": 0.1325753498901152, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1325753498901152, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 132.509, + "cuda_time_us": 43.424, + "pct_cuda_time": 0.6402304263376737, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.424, + "pct_cuda_time": 0.6402304263376737, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2313.991, + "cuda_time_us": 194.942, + "pct_cuda_time": 2.874166354345956, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.666, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.047651638216731795, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047651638216731795, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1676.662, + "cuda_time_us": 54.815, + "pct_cuda_time": 0.8081759123917552, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 132.942, + "cuda_time_us": 20.48, + "pct_cuda_time": 0.30195097483869654, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.48, + "pct_cuda_time": 0.30195097483869654, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 502.284, + "cuda_time_us": 3.52, + "pct_cuda_time": 0.05189782380040096, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.52, + "pct_cuda_time": 0.05189782380040096, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 721.185, + "cuda_time_us": 15.231, + "pct_cuda_time": 0.22456129383633724, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.4, + "pct_cuda_time": 0.03538487986390975, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.327, + "pct_cuda_time": 0.16700188925771073, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.022174524714716776, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 171.228, + "cuda_time_us": 15.584, + "pct_cuda_time": 0.2297658199163206, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.584, + "pct_cuda_time": 0.2297658199163206, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 80.151, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.047179839818546336, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047179839818546336, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 418.294, + "cuda_time_us": 133.695, + "pct_cuda_time": 1.9711589639189224, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 136.607, + "cuda_time_us": 80.447, + "pct_cuda_time": 1.1860864293383115, + "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": 80.447, + "pct_cuda_time": 1.1860864293383115, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.83, + "cuda_time_us": 8.896, + "pct_cuda_time": 0.1311599546955588, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1311599546955588, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 135.261, + "cuda_time_us": 44.352, + "pct_cuda_time": 0.6539125798850521, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.352, + "pct_cuda_time": 0.6539125798850521, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2265.341, + "cuda_time_us": 197.278, + "pct_cuda_time": 2.9086076374134944, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.759, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04670804142036087, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04670804142036087, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1627.483, + "cuda_time_us": 56.031000000000006, + "pct_cuda_time": 0.826104251522803, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 133.644, + "cuda_time_us": 21.791, + "pct_cuda_time": 0.3212799654643572, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.3212799654643572, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 495.702, + "cuda_time_us": 3.84, + "pct_cuda_time": 0.05661580778225559, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05661580778225559, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 703.137, + "cuda_time_us": 14.88, + "pct_cuda_time": 0.2193862551562404, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.4, + "pct_cuda_time": 0.03538487986390975, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 10.976, + "pct_cuda_time": 0.16182685057761392, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.022174524714716776, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 163.397, + "cuda_time_us": 15.52, + "pct_cuda_time": 0.2288222231199497, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.52, + "pct_cuda_time": 0.2288222231199497, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 77.172, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.045764444623989944, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045764444623989944, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 425.588, + "cuda_time_us": 134.975, + "pct_cuda_time": 1.9900308998463407, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 135.942, + "cuda_time_us": 81.823, + "pct_cuda_time": 1.2063737604602862, + "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": 81.823, + "pct_cuda_time": 1.2063737604602862, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.419, + "cuda_time_us": 9.312, + "pct_cuda_time": 0.13729333387196982, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.312, + "pct_cuda_time": 0.13729333387196982, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 145.966, + "cuda_time_us": 43.84, + "pct_cuda_time": 0.6463638055140848, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6463638055140848, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2607.789, + "cuda_time_us": 196.31799999999998, + "pct_cuda_time": 2.894453685467931, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.311, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.0462509867221187, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0462509867221187, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1765.628, + "cuda_time_us": 55.391000000000005, + "pct_cuda_time": 0.8166682835590938, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 130.124, + "cuda_time_us": 21.024, + "pct_cuda_time": 0.3099715476078494, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.024, + "pct_cuda_time": 0.3099715476078494, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 507.092, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053785017393142814, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.053785017393142814, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 796.364, + "cuda_time_us": 14.911999999999999, + "pct_cuda_time": 0.2198580535544259, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.368, + "pct_cuda_time": 0.034913081465724284, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.264, + "pct_cuda_time": 0.16607303616128305, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.018871935927418534, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 197.009, + "cuda_time_us": 15.807, + "pct_cuda_time": 0.2330536650036756, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.807, + "pct_cuda_time": 0.2330536650036756, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.676, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.045764444623989944, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045764444623989944, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 614.523, + "cuda_time_us": 134.68599999999998, + "pct_cuda_time": 1.985769970562728, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 142.872, + "cuda_time_us": 81.695, + "pct_cuda_time": 1.2044865668675444, + "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": 1.2044865668675444, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.011, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.13163175309374428, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13163175309374428, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 319.831, + "cuda_time_us": 44.063, + "pct_cuda_time": 0.6496516506014397, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6496516506014397, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2396.962, + "cuda_time_us": 196.509, + "pct_cuda_time": 2.8972697321571, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 99.965, + "cuda_time_us": 3.072, + "pct_cuda_time": 0.04529264622580448, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04529264622580448, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1692.981, + "cuda_time_us": 56.0, + "pct_cuda_time": 0.8256471968245608, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 140.54, + "cuda_time_us": 21.696, + "pct_cuda_time": 0.3198793139697441, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.3198793139697441, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 523.433, + "cuda_time_us": 3.68, + "pct_cuda_time": 0.05425681579132828, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05425681579132828, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 716.5, + "cuda_time_us": 15.232, + "pct_cuda_time": 0.22457603753628053, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.0372720734566516, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.392, + "pct_cuda_time": 0.16796022975402491, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.019343734325603996, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 170.643, + "cuda_time_us": 15.392, + "pct_cuda_time": 0.22693502952720784, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.392, + "pct_cuda_time": 0.22693502952720784, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.358, + "cuda_time_us": 3.135, + "pct_cuda_time": 0.0462214993222321, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0462214993222321, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 446.189, + "cuda_time_us": 134.302, + "pct_cuda_time": 1.9801083897845029, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.116, + "cuda_time_us": 81.599, + "pct_cuda_time": 1.203071171672988, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.599, + "pct_cuda_time": 1.203071171672988, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 96.59, + "cuda_time_us": 8.8, + "pct_cuda_time": 0.12974455950100242, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.8, + "pct_cuda_time": 0.12974455950100242, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.731, + "cuda_time_us": 43.903, + "pct_cuda_time": 0.6472926586105123, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.903, + "pct_cuda_time": 0.6472926586105123, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2348.774, + "cuda_time_us": 195.73800000000003, + "pct_cuda_time": 2.8859023395008196, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.052, + "cuda_time_us": 3.231, + "pct_cuda_time": 0.04763689451678849, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04763689451678849, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1642.05, + "cuda_time_us": 55.071, + "pct_cuda_time": 0.8119502995772391, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 135.665, + "cuda_time_us": 20.576, + "pct_cuda_time": 0.3033663700332529, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.576, + "pct_cuda_time": 0.3033663700332529, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 487.547, + "cuda_time_us": 3.552, + "pct_cuda_time": 0.05236962219858643, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.552, + "pct_cuda_time": 0.05236962219858643, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 692.877, + "cuda_time_us": 15.231, + "pct_cuda_time": 0.22456129383633724, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036328476660280676, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.263, + "pct_cuda_time": 0.1660582924613398, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.022174524714716776, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 182.365, + "cuda_time_us": 15.712, + "pct_cuda_time": 0.23165301350906248, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.712, + "pct_cuda_time": 0.23165301350906248, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.897, + "cuda_time_us": 3.295, + "pct_cuda_time": 0.04858049131315942, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.295, + "pct_cuda_time": 0.04858049131315942, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 463.985, + "cuda_time_us": 134.14100000000002, + "pct_cuda_time": 1.9777346540936325, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 141.88, + "cuda_time_us": 81.471, + "pct_cuda_time": 1.2011839780802462, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 81.471, + "pct_cuda_time": 1.2011839780802462, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 123.155, + "cuda_time_us": 8.991, + "pct_cuda_time": 0.13256060619017188, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 8.991, + "pct_cuda_time": 0.13256060619017188, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.947, + "cuda_time_us": 43.679, + "pct_cuda_time": 0.6439900698232142, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6439900698232142, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2375.951, + "cuda_time_us": 196.606, + "pct_cuda_time": 2.8986998710516, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.466, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.047651638216731795, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047651638216731795, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1710.629, + "cuda_time_us": 55.617000000000004, + "pct_cuda_time": 0.8200003597462785, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 146.94, + "cuda_time_us": 21.472, + "pct_cuda_time": 0.3165767251824459, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.472, + "pct_cuda_time": 0.3165767251824459, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 504.244, + "cuda_time_us": 3.585, + "pct_cuda_time": 0.052856164296715184, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.585, + "pct_cuda_time": 0.052856164296715184, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 725.907, + "cuda_time_us": 15.104, + "pct_cuda_time": 0.22268884394353866, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03585667826209521, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.2, + "pct_cuda_time": 0.16512943936491215, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.472, + "pct_cuda_time": 0.02170272631653131, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 188.248, + "cuda_time_us": 15.456, + "pct_cuda_time": 0.22787862632357878, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.456, + "pct_cuda_time": 0.22787862632357878, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 83.842, + "cuda_time_us": 3.137, + "pct_cuda_time": 0.0462509867221187, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.0462509867221187, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 437.744, + "cuda_time_us": 134.62, + "pct_cuda_time": 1.984796886366471, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 138.562, + "cuda_time_us": 81.726, + "pct_cuda_time": 1.2049436215657867, + "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": 81.726, + "pct_cuda_time": 1.2049436215657867, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 112.783, + "cuda_time_us": 9.247, + "pct_cuda_time": 0.1363349933756556, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::act_and_mul_kernel(c10::BFloat16 const&)), true>(c10::BFloat16*, c10::BFloat16 const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 9.247, + "pct_cuda_time": 0.1363349933756556, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 134.884, + "cuda_time_us": 43.647, + "pct_cuda_time": 0.6435182714250286, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.647, + "pct_cuda_time": 0.6435182714250286, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2274.36, + "cuda_time_us": 195.292, + "pct_cuda_time": 2.879326649326109, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.471, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04482084782761901, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04482084782761901, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1633.668, + "cuda_time_us": 55.199, + "pct_cuda_time": 0.8138374931699808, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 137.772, + "cuda_time_us": 21.087, + "pct_cuda_time": 0.31090040070427705, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.087, + "pct_cuda_time": 0.31090040070427705, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 479.763, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053785017393142814, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.053785017393142814, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 701.23, + "cuda_time_us": 14.815999999999999, + "pct_cuda_time": 0.21844265835986948, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.4, + "pct_cuda_time": 0.03538487986390975, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.136, + "pct_cuda_time": 0.1641858425685412, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.018871935927418534, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 171.191, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.23070941671269157, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.648, + "pct_cuda_time": 0.23070941671269157, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.338, + "cuda_time_us": 3.295, + "pct_cuda_time": 0.04858049131315942, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.295, + "pct_cuda_time": 0.04858049131315942, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 415.363, + "cuda_time_us": 133.758, + "pct_cuda_time": 1.97208781701535, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 135.085, + "cuda_time_us": 81.247, + "pct_cuda_time": 1.197881389292948, + "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": 1.197881389292948, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 93.651, + "cuda_time_us": 9.12, + "pct_cuda_time": 0.13446254348285702, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13446254348285702, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 134.326, + "cuda_time_us": 43.391, + "pct_cuda_time": 0.639743884239545, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.391, + "pct_cuda_time": 0.639743884239545, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2392.301, + "cuda_time_us": 197.118, + "pct_cuda_time": 2.906248645422567, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 67.402, + "cuda_time_us": 3.04, + "pct_cuda_time": 0.04482084782761901, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04482084782761901, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1722.617, + "cuda_time_us": 56.479, + "pct_cuda_time": 0.8327094290973993, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 131.041, + "cuda_time_us": 21.856, + "pct_cuda_time": 0.3222383059606715, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.3222383059606715, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 542.614, + "cuda_time_us": 3.904, + "pct_cuda_time": 0.05755940457862652, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.904, + "pct_cuda_time": 0.05755940457862652, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 714.36, + "cuda_time_us": 15.072, + "pct_cuda_time": 0.2222170455453532, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036800275058466135, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.264, + "pct_cuda_time": 0.16607303616128305, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.019343734325603996, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 192.377, + "cuda_time_us": 15.647, + "pct_cuda_time": 0.23069467301274826, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.647, + "pct_cuda_time": 0.23069467301274826, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 89.433, + "cuda_time_us": 3.232, + "pct_cuda_time": 0.047651638216731795, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047651638216731795, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 439.661, + "cuda_time_us": 134.367, + "pct_cuda_time": 1.9810667302808167, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 144.819, + "cuda_time_us": 81.151, + "pct_cuda_time": 1.1964659940983917, + "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": 81.151, + "pct_cuda_time": 1.1964659940983917, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 98.334, + "cuda_time_us": 8.896, + "pct_cuda_time": 0.1311599546955588, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1311599546955588, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 142.092, + "cuda_time_us": 44.32, + "pct_cuda_time": 0.6534407814868667, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.32, + "pct_cuda_time": 0.6534407814868667, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2254.212, + "cuda_time_us": 196.02800000000002, + "pct_cuda_time": 2.890178012484375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.312, + "cuda_time_us": 3.168, + "pct_cuda_time": 0.04670804142036087, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04670804142036087, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1584.383, + "cuda_time_us": 55.261, + "pct_cuda_time": 0.8147516025664653, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 139.862, + "cuda_time_us": 20.671, + "pct_cuda_time": 0.304767021527866, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.671, + "pct_cuda_time": 0.304767021527866, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 458.819, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053785017393142814, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.053785017393142814, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 681.537, + "cuda_time_us": 15.135, + "pct_cuda_time": 0.22314589864178086, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03585667826209521, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.199, + "pct_cuda_time": 0.16511469566496886, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.022174524714716776, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 165.096, + "cuda_time_us": 15.807, + "pct_cuda_time": 0.2330536650036756, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.807, + "pct_cuda_time": 0.2330536650036756, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 81.025, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.045764444623989944, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045764444623989944, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 443.759, + "cuda_time_us": 134.495, + "pct_cuda_time": 1.982953923873559, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 159.896, + "cuda_time_us": 80.863, + "pct_cuda_time": 1.1922198085147224, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 80.863, + "pct_cuda_time": 1.1922198085147224, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.975, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.13351894668648612, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13351894668648612, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 135.615, + "cuda_time_us": 44.576, + "pct_cuda_time": 0.6572151686723504, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.576, + "pct_cuda_time": 0.6572151686723504, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2181.419, + "cuda_time_us": 197.85399999999998, + "pct_cuda_time": 2.917100008580833, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 72.86, + "cuda_time_us": 3.2, + "pct_cuda_time": 0.047179839818546336, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.047179839818546336, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1552.382, + "cuda_time_us": 56.19199999999999, + "pct_cuda_time": 0.8284779872136735, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 133.332, + "cuda_time_us": 21.888, + "pct_cuda_time": 0.32271010435885694, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.888, + "pct_cuda_time": 0.32271010435885694, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 448.215, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05331321899495735, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05331321899495735, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 672.289, + "cuda_time_us": 15.168, + "pct_cuda_time": 0.2236324407399096, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.03585667826209521, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.232, + "pct_cuda_time": 0.1656012377630976, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.022174524714716776, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 164.816, + "cuda_time_us": 15.52, + "pct_cuda_time": 0.2288222231199497, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.52, + "pct_cuda_time": 0.2288222231199497, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.216, + "cuda_time_us": 3.167, + "pct_cuda_time": 0.04669329772041757, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.167, + "pct_cuda_time": 0.04669329772041757, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 412.353, + "cuda_time_us": 135.295, + "pct_cuda_time": 1.9947488838281953, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 136.54, + "cuda_time_us": 81.823, + "pct_cuda_time": 1.2063737604602862, + "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": 81.823, + "pct_cuda_time": 1.2063737604602862, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 94.55, + "cuda_time_us": 9.024, + "pct_cuda_time": 0.13304714828830064, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13304714828830064, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 133.462, + "cuda_time_us": 44.448, + "pct_cuda_time": 0.6553279750796085, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6553279750796085, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2560.417, + "cuda_time_us": 195.93200000000002, + "pct_cuda_time": 2.8887626172898186, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 69.944, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04434904942943355, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.008, + "pct_cuda_time": 0.04434904942943355, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1910.241, + "cuda_time_us": 55.519000000000005, + "pct_cuda_time": 0.8185554771518355, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 138.93, + "cuda_time_us": 20.704, + "pct_cuda_time": 0.30525356362599476, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.704, + "pct_cuda_time": 0.30525356362599476, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 497.848, + "cuda_time_us": 3.679, + "pct_cuda_time": 0.054242072091384985, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.054242072091384985, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 941.399, + "cuda_time_us": 15.424, + "pct_cuda_time": 0.2274068279253933, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.688, + "pct_cuda_time": 0.03963106544757892, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.232, + "pct_cuda_time": 0.1656012377630976, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.504, + "pct_cuda_time": 0.022174524714716776, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 189.944, + "cuda_time_us": 15.712, + "pct_cuda_time": 0.23165301350906248, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.712, + "pct_cuda_time": 0.23165301350906248, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 79.236, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04623624302217541, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04623624302217541, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 429.771, + "cuda_time_us": 134.269, + "pct_cuda_time": 1.979621847686374, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 140.816, + "cuda_time_us": 81.022, + "pct_cuda_time": 1.1945640568057065, + "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": 81.022, + "pct_cuda_time": 1.1945640568057065, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.373, + "cuda_time_us": 9.152, + "pct_cuda_time": 0.1349343418810425, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1349343418810425, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.416, + "cuda_time_us": 44.095, + "pct_cuda_time": 0.650123448999625, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.095, + "pct_cuda_time": 0.650123448999625, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2218.781, + "cuda_time_us": 196.637, + "pct_cuda_time": 2.899156925749842, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.85, + "cuda_time_us": 3.296, + "pct_cuda_time": 0.048595235013102714, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.048595235013102714, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1586.752, + "cuda_time_us": 56.702999999999996, + "pct_cuda_time": 0.8360120178846976, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 132.988, + "cuda_time_us": 22.4, + "pct_cuda_time": 0.3302588787298243, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 22.4, + "pct_cuda_time": 0.3302588787298243, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 473.047, + "cuda_time_us": 3.712, + "pct_cuda_time": 0.05472861418951375, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.05472861418951375, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 680.317, + "cuda_time_us": 15.104, + "pct_cuda_time": 0.22268884394353866, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.0372720734566516, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.264, + "pct_cuda_time": 0.16607303616128305, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.312, + "pct_cuda_time": 0.019343734325603996, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 164.175, + "cuda_time_us": 15.487, + "pct_cuda_time": 0.22833568102182092, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.487, + "pct_cuda_time": 0.22833568102182092, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 75.713, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.045764444623989944, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045764444623989944, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 419.956, + "cuda_time_us": 133.534, + "pct_cuda_time": 1.9687852282280518, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 134.032, + "cuda_time_us": 81.503, + "pct_cuda_time": 1.201655776478432, + "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": 81.503, + "pct_cuda_time": 1.201655776478432, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 92.555, + "cuda_time_us": 8.799, + "pct_cuda_time": 0.1297298158010591, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1297298158010591, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 144.955, + "cuda_time_us": 43.232, + "pct_cuda_time": 0.6373996359485609, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 43.232, + "pct_cuda_time": 0.6373996359485609, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2366.551, + "cuda_time_us": 196.477, + "pct_cuda_time": 2.896797933758915, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 65.178, + "cuda_time_us": 3.423, + "pct_cuda_time": 0.050467684905901276, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.423, + "pct_cuda_time": 0.050467684905901276, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1717.631, + "cuda_time_us": 55.36, + "pct_cuda_time": 0.8162112288608515, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 128.078, + "cuda_time_us": 20.736, + "pct_cuda_time": 0.3057253620241802, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.736, + "pct_cuda_time": 0.3057253620241802, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 495.661, + "cuda_time_us": 3.52, + "pct_cuda_time": 0.05189782380040096, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.52, + "pct_cuda_time": 0.05189782380040096, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 695.672, + "cuda_time_us": 15.232, + "pct_cuda_time": 0.22457603753628053, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.036328476660280676, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.296, + "pct_cuda_time": 0.16654483455946853, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.472, + "pct_cuda_time": 0.02170272631653131, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 267.213, + "cuda_time_us": 15.872, + "pct_cuda_time": 0.2340120054999898, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.872, + "pct_cuda_time": 0.2340120054999898, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 85.381, + "cuda_time_us": 3.264, + "pct_cuda_time": 0.048123436614917255, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.048123436614917255, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 429.1, + "cuda_time_us": 134.43, + "pct_cuda_time": 1.9819955833772447, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 141.345, + "cuda_time_us": 81.886, + "pct_cuda_time": 1.207302613556714, + "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": 81.886, + "pct_cuda_time": 1.207302613556714, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 100.266, + "cuda_time_us": 8.96, + "pct_cuda_time": 0.13210355149192973, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13210355149192973, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 137.713, + "cuda_time_us": 43.584, + "pct_cuda_time": 0.6425894183286011, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "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": 0.6425894183286011, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2313.215, + "cuda_time_us": 197.72500000000002, + "pct_cuda_time": 2.915198071288148, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 68.098, + "cuda_time_us": 3.136, + "pct_cuda_time": 0.04623624302217541, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04623624302217541, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1663.353, + "cuda_time_us": 55.902, + "pct_cuda_time": 0.8242023142301179, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 135.734, + "cuda_time_us": 21.472, + "pct_cuda_time": 0.3165767251824459, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 21.472, + "pct_cuda_time": 0.3165767251824459, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 507.937, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05331321899495735, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_embedding_kernel(long const*, c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, int, long, long, int, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.616, + "pct_cuda_time": 0.05331321899495735, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 710.776, + "cuda_time_us": 15.197999999999999, + "pct_cuda_time": 0.22407475173820846, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 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": 0.035841934562151914, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.295, + "pct_cuda_time": 0.16653009085952525, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.472, + "pct_cuda_time": 0.02170272631653131, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 169.443, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.2302376183145061, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.616, + "pct_cuda_time": 0.2302376183145061, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 76.657, + "cuda_time_us": 3.328, + "pct_cuda_time": 0.04906703341128818, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.328, + "pct_cuda_time": 0.04906703341128818, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 433.802, + "cuda_time_us": 135.359, + "pct_cuda_time": 1.9956924806245664, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 136.306, + "cuda_time_us": 81.151, + "pct_cuda_time": 1.1964659940983917, + "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": 81.151, + "pct_cuda_time": 1.1964659940983917, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 112.217, + "cuda_time_us": 9.248, + "pct_cuda_time": 0.1363497370755989, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.1363497370755989, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 136.444, + "cuda_time_us": 44.96, + "pct_cuda_time": 0.662876749450576, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.96, + "pct_cuda_time": 0.662876749450576, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "LlamaDecoderLayer", + "cpu_time_us": 2353.317, + "cuda_time_us": 195.676, + "pct_cuda_time": 2.8849882301043346, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 71.518, + "cuda_time_us": 3.103, + "pct_cuda_time": 0.04574970092404665, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.04574970092404665, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaAttention", + "cpu_time_us": 1687.419, + "cuda_time_us": 55.327999999999996, + "pct_cuda_time": 0.815739430462666, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "QKVParallelLinear(weight=bfloat16[6144, 4096])", + "cpu_time_us": 130.437, + "cuda_time_us": 20.992, + "pct_cuda_time": 0.30949974920966394, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 20.992, + "pct_cuda_time": 0.30949974920966394, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 6144]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 6144]) <- linear(bfloat16[12, 4096], bfloat16[6144, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Llama3RotaryEmbedding", + "cpu_time_us": 502.806, + "cuda_time_us": 3.648, + "pct_cuda_time": 0.053785017393142814, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::rotary_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": 0.053785017393142814, + "trace": "_C::rotary_embedding(int64[12], bfloat16[12, 4096], bfloat16[12, 1024], 128, bfloat16[131072, 128], True)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Attention", + "cpu_time_us": 713.763, + "cuda_time_us": 14.751999999999999, + "pct_cuda_time": 0.21749906156349857, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0>(__nv_bfloat16 const*, __nv_bfloat16 const*, __nv_bfloat16*, __nv_bfloat16*, long const*, int, int, int, int, int, int, float const*, float const*)", + "cpu_time_us": 0, + "cuda_time_us": 2.4, + "pct_cuda_time": 0.03538487986390975, + "trace": "_C_cache_ops::reshape_and_cache_flash(bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], int64[12], None, float32[], float32[]) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void cutlass::device_kernel, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > > >(flash::enable_sm90_or_later, cute::C<1>, cute::C<1> >, cute::tuple, cute::C<128>, cute::C<128> >, cutlass::bfloat16_t, float, cutlass::arch::Sm90, true, false, false, true, true, false, true, true, true, false, false>, flash::CollectiveEpilogueFwd, cute::C<128>, cute::C<128> >, cute::tuple, cute::C<1>, cute::C<1> >, cutlass::bfloat16_t, cutlass::arch::Sm90, 256, true, true, false>, flash::VarlenDynamicPersistentTileScheduler<128, 256, 128, false, true, true> > >::Params)", + "cpu_time_us": 0, + "cuda_time_us": 11.072, + "pct_cuda_time": 0.16324224577217028, + "trace": "_vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor, at::detail::Array >(int, at::native::FillFunctor, at::detail::Array)", + "cpu_time_us": 0, + "cuda_time_us": 1.28, + "pct_cuda_time": 0.018871935927418534, + "trace": "fill_(int32[1], 0) <- zero_(int32[1]) <- zeros(None, 3, 0, None, None) <- _vllm_fa3_C::fwd(bfloat16[12, 1, 32, 128], bfloat16[28102, 16, 8, 128], bfloat16[28102, 16, 8, 128], None, None, bfloat16[12, 1, 32, 128], None, None, None, None, int32[12], None, None, int32[12, 9], None, None, None, None, None, None, None, 0.08838834764831845, True, -1, -1, 0, 0.0, True, 0, None, 0) <- vllm::unified_attention_with_output(bfloat16[12, 32, 128], bfloat16[12, 8, 128], bfloat16[12, 8, 128], bfloat16[12, 32, 128], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 4096])", + "cpu_time_us": 189.541, + "cuda_time_us": 15.936, + "pct_cuda_time": 0.23495560229636073, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 15.936, + "pct_cuda_time": 0.23495560229636073, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 4096]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 4096]) <- linear(bfloat16[12, 4096], bfloat16[4096, 4096], None)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 86.565, + "cuda_time_us": 3.104, + "pct_cuda_time": 0.045764444623989944, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type 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": 0.045764444623989944, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "LlamaMLP", + "cpu_time_us": 433.34, + "cuda_time_us": 134.141, + "pct_cuda_time": 1.977734654093632, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "MergedColumnParallelLinear(weight=bfloat16[28672, 4096])", + "cpu_time_us": 142.52, + "cuda_time_us": 80.83, + "pct_cuda_time": 1.1917332664165936, + "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": 80.83, + "pct_cuda_time": 1.1917332664165936, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 28672]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 28672]) <- linear(bfloat16[12, 4096], bfloat16[28672, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "SiluAndMul", + "cpu_time_us": 97.026, + "cuda_time_us": 9.216, + "pct_cuda_time": 0.13587793867741343, + "trace": "" + }, + "children": [ + { + "entry": { + "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": 0.13587793867741343, + "trace": "_C::silu_and_mul(bfloat16[12, 14336], bfloat16[12, 28672])" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "RowParallelLinear(weight=bfloat16[4096, 14336])", + "cpu_time_us": 137.772, + "cuda_time_us": 44.095, + "pct_cuda_time": 0.650123448999625, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 44.095, + "pct_cuda_time": 0.650123448999625, + "trace": "mm(bfloat16[12, 14336], bfloat16[14336, 4096]) <- matmul(bfloat16[12, 14336], bfloat16[14336, 4096]) <- linear(bfloat16[12, 14336], bfloat16[4096, 14336], None)" + }, + "children": [] + } + ] + } + ] + } + ] + }, + { + "entry": { + "name": "RMSNorm(weight=bfloat16[4096])", + "cpu_time_us": 73.038, + "cuda_time_us": 3.073, + "pct_cuda_time": 0.04530738992574777, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "std::enable_if<(((8)>(0)))&&vllm::_typeConvert::exists, void>::type vllm::fused_add_rms_norm_kernel(c10::BFloat16*, c10::BFloat16*, c10::BFloat16 const*, float, int, int)", + "cpu_time_us": 0, + "cuda_time_us": 3.073, + "pct_cuda_time": 0.04530738992574777, + "trace": "_C::fused_add_rms_norm(bfloat16[12, 4096], bfloat16[12, 4096], bfloat16[4096], 1e-05)" + }, + "children": [] + } + ] + } + ] + }, + { + "entry": { + "name": "LogitsProcessor", + "cpu_time_us": 485.092, + "cuda_time_us": 351.38800000000003, + "pct_cuda_time": 5.1807592356748, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "void at::native::(anonymous namespace)::indexSelectSmallIndex(at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, at::cuda::detail::TensorInfo, int, int, unsigned int, long)", + "cpu_time_us": 0, + "cuda_time_us": 7.231, + "pct_cuda_time": 0.1066116942899714, + "trace": "index_select(bfloat16[12, 4096], 0, int64[12])" + }, + "children": [] + }, + { + "entry": { + "name": "Memset (Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.737, + "pct_cuda_time": 0.010866106858208952, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[12, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + }, + { + "entry": { + "name": "sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off_kernel__5x_cublas", + "cpu_time_us": 0, + "cuda_time_us": 343.42, + "pct_cuda_time": 5.063281434526619, + "trace": "mm(bfloat16[12, 4096], bfloat16[4096, 128256]) <- matmul(bfloat16[12, 4096], bfloat16[4096, 128256]) <- linear(bfloat16[12, 4096], bfloat16[128256, 4096], None)" + }, + "children": [] + } + ] + }, + { + "entry": { + "name": "Sampler", + "cpu_time_us": 3941.07, + "cuda_time_us": 124.896, + "pct_cuda_time": 1.841429148117863, + "trace": "" + }, + "children": [ + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.010851363158265655, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.736, + "pct_cuda_time": 0.010851363158265655, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.011794959954636584, + "trace": "copy_(int32[12], int32[12], True) <- _to_copy(int32[12], 3, 0, None, None, True, None) <- to(int32[12], 3, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.011794959954636584, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.011794959954636584, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.769, + "pct_cuda_time": 0.011337905256394415, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy HtoD (Pinned -> Device)", + "cpu_time_us": 0, + "cuda_time_us": 0.8, + "pct_cuda_time": 0.011794959954636584, + "trace": "copy_(bfloat16[12], bfloat16[12], True) <- _to_copy(bfloat16[12], 15, 0, None, None, True, None) <- to(bfloat16[12], 15, 0, None, None, True, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 6.784, + "pct_cuda_time": 0.10002126041531822, + "trace": "copy_(float32[12, 128256], bfloat16[12, 128256], False) <- _to_copy(bfloat16[12, 128256], 6, None, None, None, False, None) <- to(bfloat16[12, 128256], 6, False, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::elementwise_kernel<128, 4, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl > >(at::TensorIteratorBase&, at::native::BinaryFunctor > const&)::{lambda(int)#1})", + "cpu_time_us": 0, + "cuda_time_us": 8.928, + "pct_cuda_time": 0.13163175309374428, + "trace": "div_(float32[12, 128256], bfloat16[12, 1])" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::SoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 34.816, + "pct_cuda_time": 0.5133166572257841, + "trace": "_softmax(float32[12, 128256], -1, False) <- softmax(float32[12, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::(anonymous namespace)::cunn_SoftMaxForward<4, float, float, float, at::native::(anonymous namespace)::LogSoftMaxForwardEpilogue>(float*, float const*, int)", + "cpu_time_us": 0, + "cuda_time_us": 28.192, + "pct_cuda_time": 0.41565438880139316, + "trace": "_log_softmax(float32[12, 128256], -1, False) <- log_softmax(float32[12, 128256], -1, 6)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::unrolled_elementwise_kernel, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, at::detail::Array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "cpu_time_us": 0, + "cuda_time_us": 1.824, + "pct_cuda_time": 0.026892508696571407, + "trace": "copy_(int64[12], int32[12], False) <- _to_copy(int32[12], 4, None, None, None, False, None) <- to(int32[12], 4, False, False, None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::index_elementwise_kernel<128, 4, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1}>(long, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&)::{lambda(int)#1})", + "cpu_time_us": 0, + "cuda_time_us": 9.056, + "pct_cuda_time": 0.13351894668648612, + "trace": "index(float32[12, 128256], None)" + }, + "children": [] + }, + { + "entry": { + "name": "void at::native::reduce_kernel<512, 1, at::native::ReduceOp, unsigned int, long, 4> >(at::native::ReduceOp, unsigned int, long, 4>)", + "cpu_time_us": 0, + "cuda_time_us": 27.295, + "pct_cuda_time": 0.4024292899522569, + "trace": "argmax(float32[12, 128256], -1, False)" + }, + "children": [] + }, + { + "entry": { + "name": "Memcpy DtoH (Device -> Pageable)", + "cpu_time_us": 0, + "cuda_time_us": 2.56, + "pct_cuda_time": 0.03774387185483707, + "trace": "copy_(int64[12], int64[12], False) <- _to_copy(int64[12], 4, 0, None, None, False, None) <- to(int64[12], 4, 0, None, None, False, False, None)" + }, + "children": [] + } + ] + } + ] + } +} \ No newline at end of file