From 253391a650e62663e4c5a967bceccce31b92f8b1 Mon Sep 17 00:00:00 2001 From: LiaoYFBH <2273398935@qq.com> Date: Mon, 9 Mar 2026 20:21:43 +0800 Subject: [PATCH 01/13] feat: integrate FlashAttention v2 with fwd/bwd CUDA kernels, GQA support Implement hand-written FlashAttention v2 CUDA kernels (forward + backward) integrated into InfiniTrain's Dispatcher + Autograd system. Supports causal masking, configurable scale, dropout, and Grouped Query Attention (GQA). New files: - autograd/scaled_dot_product_attention.{h,cc}: Autograd Function with Forward/SetupContext/Backward, dispatching to registered CUDA kernels - kernels/cuda/scaled_dot_product_attention.cu: Tiled FlashAttention v2 kernels with online softmax (fwd) and recomputation-based backward, float32 gradient buffers for atomicAdd safety with bf16/GQA Modified files: - nn/functional.{h,cc}: Add ScaledDotProductAttention API (PyTorch-compatible) - example/gpt2/{main,net}.{h,cc}: Add --flash flag, flash attention branch in CausalSelfAttention, skip causal mask buffer when flash is enabled - example/llama3/{main,net}.{h,cc}: Add --flash flag, native GQA support in flash path (no RepeatKV needed) - docs/flash_attention_design.md: Design document with architecture, algorithm details, and performance results Performance (GPT-2 124M, A100, seq=256, bs=4): - Step time: 174ms -> 126ms (-28%) - Throughput: 5880 -> 8100 tok/s (+38%) --- example/gpt2/main.cc | 6 +- example/gpt2/net.cc | 43 +- example/gpt2/net.h | 3 +- example/llama3/main.cc | 5 +- example/llama3/net.cc | 72 +- example/llama3/net.h | 2 +- .../autograd/scaled_dot_product_attention.h | 46 ++ infini_train/include/nn/functional.h | 22 + .../autograd/scaled_dot_product_attention.cc | 88 +++ .../cuda/scaled_dot_product_attention.cu | 637 ++++++++++++++++++ infini_train/src/nn/functional.cc | 9 + 11 files changed, 886 insertions(+), 47 deletions(-) create mode 100644 infini_train/include/autograd/scaled_dot_product_attention.h create mode 100644 infini_train/src/autograd/scaled_dot_product_attention.cc create mode 100644 infini_train/src/kernels/cuda/scaled_dot_product_attention.cu diff --git a/example/gpt2/main.cc b/example/gpt2/main.cc index 69f8ba7e..db21f59c 100644 --- a/example/gpt2/main.cc +++ b/example/gpt2/main.cc @@ -78,6 +78,8 @@ DEFINE_string(dtype, "float32", "precision used in training (float32/bfloat16)") DEFINE_string( precision_check, "", "precision check config: level=N,format=simple|table,output_md5=true|false,output_path=PATH,baseline=PATH"); +// flash attention +DEFINE_bool(flash, false, "Enable FlashAttention for CausalSelfAttention"); using namespace infini_train; @@ -179,11 +181,13 @@ void Train(const nn::parallel::Rank &rank) { // init the model, either from scratch or from OpenAI pretrained checkpoint GPT2Config model_config; + model_config.flash = FLAGS_flash; std::shared_ptr model = nullptr; if (!FLAGS_llmc_filepath.empty()) { - model = GPT2::FromLLMC(FLAGS_llmc_filepath); + model = GPT2::FromLLMC(FLAGS_llmc_filepath, FLAGS_flash); } else if (kModelToConfigs.count(FLAGS_model)) { model_config = kModelToConfigs.at(FLAGS_model); + model_config.flash = FLAGS_flash; model = std::make_shared(model_config); } else { model = GPT2::FromPretrained(kStrToModelType.at(FLAGS_model)); diff --git a/example/gpt2/net.cc b/example/gpt2/net.cc index 8d497797..781fac1d 100644 --- a/example/gpt2/net.cc +++ b/example/gpt2/net.cc @@ -73,9 +73,11 @@ CausalSelfAttention::CausalSelfAttention(const GPT2Config &config) /*skip_bias_add=*/false, /*sequence_parallel=*/nn::parallel::global::GetSequenceParallelEnabled()); - // causal mask: (1, 1, block_size, block_size) - buffers_[kParamBiasName] = nn::function::Tril(nn::function::Ones({config_.block_size, config_.block_size})) - ->View({1, 1, config_.block_size, config_.block_size}); + // causal mask: only needed when not using flash attention + if (!config.flash) { + buffers_[kParamBiasName] = nn::function::Tril(nn::function::Ones({config_.block_size, config_.block_size})) + ->View({1, 1, config_.block_size, config_.block_size}); + } } std::vector> @@ -105,16 +107,26 @@ CausalSelfAttention::Forward(const std::vectorView({B, T, local_n_head_, head_dim})->Transpose(1, 2); v = v->View({B, T, local_n_head_, head_dim})->Transpose(1, 2); - // (B, h_l, T, T) - auto att = q->Matmul(k->Transpose(-2, -1)) * (1.0 / std::sqrt(head_dim)); - // (1, 1, T, T) - auto mask = buffers_[kParamBiasName]->Slice({0, 0, 0, 0}, {1, 1, T, T}, {1, 1, 1, 1}); - // (1, 1, T, T) -> eq 0 -> (1, 1, T, T) -> masked_fill -> (B, h_l, T, T) - att = att->MaskedFill(mask == 0, -std::numeric_limits::infinity()); - // (B, h_l, T, T) - att = nn::function::Softmax(att, -1); - // (B, h_l, T, Dh) - auto y = att->Matmul(v); + std::shared_ptr y; + + if (config_.flash) { + // FlashAttention path: fused scaled dot-product attention with causal mask + // Q, K, V: (B, h_l, T, Dh) -> O: (B, h_l, T, Dh) + y = nn::function::ScaledDotProductAttention(q, k, v, /*is_causal=*/true); + } else { + // Original small-operator path + // (B, h_l, T, T) + auto att = q->Matmul(k->Transpose(-2, -1)) * (1.0 / std::sqrt(head_dim)); + // (1, 1, T, T) + auto mask = buffers_[kParamBiasName]->Slice({0, 0, 0, 0}, {1, 1, T, T}, {1, 1, 1, 1}); + // (1, 1, T, T) -> eq 0 -> (1, 1, T, T) -> masked_fill -> (B, h_l, T, T) + att = att->MaskedFill(mask == 0, -std::numeric_limits::infinity()); + // (B, h_l, T, T) + att = nn::function::Softmax(att, -1); + // (B, h_l, T, Dh) + y = att->Matmul(v); + } + // (B, h_l, T, Dh) -> (B, T, h_l, Dh) -> (B, T, local_C) y = y->Transpose(1, 2)->Contiguous()->View({B, T, local_C}); @@ -351,7 +363,7 @@ std::tuple DetermineAndCheckVersion(const std:: } } // namespace -std::shared_ptr GPT2::FromLLMC(const std::string &filepath) { +std::shared_ptr GPT2::FromLLMC(const std::string &filepath, bool flash) { if (!std::filesystem::exists(filepath)) { LOG(FATAL) << "File not found: " << filepath; } @@ -379,7 +391,8 @@ std::shared_ptr GPT2::FromLLMC(const std::string &filepath) { .original_vocab_size = vocab_size, .n_layer = n_layer, .n_head = n_head, - .n_embd = n_embd}); + .n_embd = n_embd, + .flash = flash}); LOG(INFO) << "magic: " << magic << " version: " << version << " block_size: " << block_size << " vocab_size: " << vocab_size << " n_layer: " << n_layer << " n_head: " << n_head diff --git a/example/gpt2/net.h b/example/gpt2/net.h index 4faf5451..e429770a 100644 --- a/example/gpt2/net.h +++ b/example/gpt2/net.h @@ -19,6 +19,7 @@ struct GPT2Config { int64_t n_layer = 12; int64_t n_head = 12; int64_t n_embd = 768; + bool flash = false; }; class NewGELU : public infini_train::nn::CloneableModule { @@ -140,7 +141,7 @@ class GPT2 : public infini_train::nn::CloneableModule { Forward(const std::vector> &x) override; static std::shared_ptr FromPretrained(ModelType model_type); - static std::shared_ptr FromLLMC(const std::string &filepath); + static std::shared_ptr FromLLMC(const std::string &filepath, bool flash = false); int GetChunkSize() const; diff --git a/example/llama3/main.cc b/example/llama3/main.cc index 6d4c9a7b..018a24f8 100644 --- a/example/llama3/main.cc +++ b/example/llama3/main.cc @@ -76,6 +76,8 @@ DEFINE_string(dtype, "float32", "precision used in training (float32/bfloat16)") DEFINE_string( precision_check, "", "precision check config: level=N,format=simple|table,output_md5=true|false,output_path=PATH,baseline=PATH"); +// flash attention +DEFINE_bool(flash, false, "Enable FlashAttention for CausalSelfAttention"); using namespace infini_train; @@ -161,9 +163,10 @@ void Train(const nn::parallel::Rank &rank) { // ManualSeed(42); LLaMA3Config model_config = LLaMA3Config(); + model_config.flash = FLAGS_flash; std::shared_ptr model = nullptr; if (!FLAGS_llmc_filepath.empty()) { - model = LLaMA3::FromLLMC(FLAGS_llmc_filepath); + model = LLaMA3::FromLLMC(FLAGS_llmc_filepath, FLAGS_flash); } else { model = std::make_shared(model_config); } diff --git a/example/llama3/net.cc b/example/llama3/net.cc index a50fb831..c2790d9f 100644 --- a/example/llama3/net.cc +++ b/example/llama3/net.cc @@ -207,34 +207,49 @@ std::vector> CausalSelfAttention::Forward(const std::vec // TODO(zbl): use kv cache during inference // if (use_kv_) { ... } - // align n_head in GQA - // (B, T, KV_local, D) -> (B, T, H_local, D) via RepeatKV - k = RepeatKV(k, n_rep_); - v = RepeatKV(v, n_rep_); - - // (B, T, H_local, D) -> (B, H_local, T, D) - q = q->Transpose(1, 2); - k = k->Transpose(1, 2); - v = v->Transpose(1, 2); - - // TODO(zbl): support flash attention later - // if (flash_) { ... } - - // manual implementation of attention - // this materializes the large (T,T) matrix for all the queries and keys - - // q: (B, H_local, T, D) - // k: (B, H_local, T, D) -> (B, H_local, D, T) - // q @ k.T: (B, H_local, T, T) -> mul 1.0 / sqrt(D) -> (B, H_local, T, T) - auto att = q->Matmul(k->Transpose(-2, -1)) * (1.0 / std::sqrt(static_cast(D))); - if (mask) { - // mask: (1, 1, T, T) - att = att->MaskedFill(mask, std::numeric_limits::lowest()); + std::shared_ptr y; + + if (config_.flash) { + // FlashAttention path with native GQA support + // No need for RepeatKV - FlashAttention handles GQA internally + // (B, T, H_local, D) -> (B, H_local, T, D) + q = q->Transpose(1, 2); + // (B, T, KV_local, D) -> (B, KV_local, T, D) + k = k->Transpose(1, 2); + v = v->Transpose(1, 2); + + // Q: (B, H_local, T, D), K: (B, KV_local, T, D), V: (B, KV_local, T, D) + // FlashAttention with causal mask and GQA + y = nn::function::ScaledDotProductAttention(q, k, v, /*is_causal=*/true); + } else { + // Original small-operator path + // align n_head in GQA + // (B, T, KV_local, D) -> (B, T, H_local, D) via RepeatKV + k = RepeatKV(k, n_rep_); + v = RepeatKV(v, n_rep_); + + // (B, T, H_local, D) -> (B, H_local, T, D) + q = q->Transpose(1, 2); + k = k->Transpose(1, 2); + v = v->Transpose(1, 2); + + // manual implementation of attention + // this materializes the large (T,T) matrix for all the queries and keys + + // q: (B, H_local, T, D) + // k: (B, H_local, T, D) -> (B, H_local, D, T) + // q @ k.T: (B, H_local, T, T) -> mul 1.0 / sqrt(D) -> (B, H_local, T, T) + auto att = q->Matmul(k->Transpose(-2, -1)) * (1.0 / std::sqrt(static_cast(D))); + if (mask) { + // mask: (1, 1, T, T) + att = att->MaskedFill(mask, std::numeric_limits::lowest()); + } + // (B, H_local, T, T) + att = nn::function::Softmax(att, -1); + // att: (B, H_local, T, T) @ v: (B, H_local, T, D) -> y: (B, H_local, T, D) + y = att->Matmul(v); } - // (B, H_local, T, T) - att = nn::function::Softmax(att, -1); - // att: (B, H_local, T, T) @ v: (B, H_local, T, D) -> y: (B, H_local, T, D) - auto y = att->Matmul(v); + // (B, H_local, T, D) -> Transpose(1, 2) -> (B, T, H_local, D) -> (B, T, C_local) y = y->Transpose(1, 2)->Contiguous()->View({B, T, C_local}); // output projection @@ -457,7 +472,7 @@ constexpr int32_t kLLaMA3Magic = 20240803; constexpr int32_t kLLaMA3FP32Version = 3; } // namespace -std::shared_ptr LLaMA3::FromLLMC(const std::string &filepath) { +std::shared_ptr LLaMA3::FromLLMC(const std::string &filepath, bool flash) { if (!std::filesystem::exists(filepath)) { LOG(FATAL) << "File not found: " << filepath; } @@ -496,6 +511,7 @@ std::shared_ptr LLaMA3::FromLLMC(const std::string &filepath) { .rope_theta = rope_theta, .use_scaled_rope = static_cast(use_scaled_rope), .norm_eps = norm_eps, + .flash = flash, .max_gen_batch_size = max_gen_bs}); // ========== pp_size：num_stages; vpp_size: num_chunks_per_stage ========== diff --git a/example/llama3/net.h b/example/llama3/net.h index 4496a68d..8338913d 100644 --- a/example/llama3/net.h +++ b/example/llama3/net.h @@ -179,7 +179,7 @@ class LLaMA3 : public infini_train::nn::CloneableModule { Forward(const std::vector> &x) override; static std::shared_ptr FromPretrained(ModelType model_type); - static std::shared_ptr FromLLMC(const std::string &filepath); + static std::shared_ptr FromLLMC(const std::string &filepath, bool flash = false); int GetChunkSize() const { return stage_info_.layer_ranges_per_chunk.size(); } diff --git a/infini_train/include/autograd/scaled_dot_product_attention.h b/infini_train/include/autograd/scaled_dot_product_attention.h new file mode 100644 index 00000000..5e136e14 --- /dev/null +++ b/infini_train/include/autograd/scaled_dot_product_attention.h @@ -0,0 +1,46 @@ +#pragma once + +#include +#include +#include +#include + +#include "infini_train/include/autograd/function.h" + +namespace infini_train { +class Tensor; +} + +namespace infini_train::autograd { + +// Autograd function for scaled dot-product attention (FlashAttention). +// +// Implements the forward and backward passes of the fused attention kernel, +// compatible with PyTorch's torch.nn.functional.scaled_dot_product_attention. +// +// Supports: causal masking, dropout, custom scale factor, and GQA +// (Q may have more heads than K/V). +class ScaledDotProductAttention : public Function { +public: + static constexpr char kType[] = "ScaledDotProductAttentionFunction"; + + // Args: + // is_causal: If true, applies a causal (lower-triangular) attention mask. + // dropout_p: Dropout probability applied to attention weights (0.0 = no dropout). + // scale: Optional scaling factor for QK^T. Defaults to 1/sqrt(head_dim). + ScaledDotProductAttention(bool is_causal = false, float dropout_p = 0.0f, + std::optional scale = std::nullopt) + : Function(kType), is_causal_(is_causal), dropout_p_(dropout_p), scale_(scale) {} + + std::vector> Forward(const std::vector> &input_tensors) override; + void SetupContext(const std::vector> &input_tensors, + const std::vector> &output_tensors) override; + std::vector> Backward(const std::vector> &grad_outputs) override; + +private: + bool is_causal_ = false; + float dropout_p_ = 0.0f; + std::optional scale_; +}; + +} // namespace infini_train::autograd diff --git a/infini_train/include/nn/functional.h b/infini_train/include/nn/functional.h index e4354fd1..954226cf 100644 --- a/infini_train/include/nn/functional.h +++ b/infini_train/include/nn/functional.h @@ -2,6 +2,7 @@ #include #include +#include #include namespace infini_train { @@ -183,4 +184,25 @@ std::shared_ptr Stack(const std::vector> &inputs // Concatenation of the input tensors. std::shared_ptr Concat(const std::vector> &inputs, int64_t dim = 0); +// Computes scaled dot-product attention using fused FlashAttention kernel. +// +// This function is compatible with PyTorch's torch.nn.functional.scaled_dot_product_attention. +// When is_causal is true, a causal (lower-triangular) mask is applied. +// +// Args: +// query: [B, H_q, N, d] query tensor. +// key: [B, H_kv, N, d] key tensor (H_kv <= H_q for GQA). +// value: [B, H_kv, N, d] value tensor. +// is_causal: Apply causal attention mask (default false). +// dropout_p: Dropout probability on attention weights (default 0.0). +// scale: Scaling factor for QK^T. Defaults to 1/sqrt(d) if not provided. +// +// Returns: +// Attention output tensor [B, H_q, N, d]. +std::shared_ptr ScaledDotProductAttention(const std::shared_ptr &query, + const std::shared_ptr &key, + const std::shared_ptr &value, bool is_causal = false, + float dropout_p = 0.0f, + std::optional scale = std::nullopt); + } // namespace infini_train::nn::function diff --git a/infini_train/src/autograd/scaled_dot_product_attention.cc b/infini_train/src/autograd/scaled_dot_product_attention.cc new file mode 100644 index 00000000..4b140dcf --- /dev/null +++ b/infini_train/src/autograd/scaled_dot_product_attention.cc @@ -0,0 +1,88 @@ +#include "infini_train/include/autograd/scaled_dot_product_attention.h" + +#include +#include +#include +#include + +#include "glog/logging.h" + +#include "infini_train/include/dispatcher.h" +#include "infini_train/include/tensor.h" + +namespace infini_train::autograd { + +std::vector> +ScaledDotProductAttention::Forward(const std::vector> &input_tensors) { + CHECK_EQ(input_tensors.size(), 3) << "ScaledDotProductAttention expects 3 inputs: Q, K, V"; + + const auto &query = input_tensors[0]; + const auto &key = input_tensors[1]; + const auto &value = input_tensors[2]; + + // Q: [B, H_q, N, d], K: [B, H_kv, N, d], V: [B, H_kv, N, d] + CHECK_EQ(query->Dims().size(), 4) << "Query must be 4D [B, H, N, d]"; + CHECK_EQ(key->Dims().size(), 4) << "Key must be 4D [B, H, N, d]"; + CHECK_EQ(value->Dims().size(), 4) << "Value must be 4D [B, H, N, d]"; + + const auto B = query->Dims()[0]; + const auto H_q = query->Dims()[1]; + const auto N = query->Dims()[2]; + const auto d = query->Dims()[3]; + const auto H_kv = key->Dims()[1]; + + CHECK_EQ(key->Dims()[0], B); + CHECK_EQ(value->Dims()[0], B); + CHECK_EQ(key->Dims()[2], N); + CHECK_EQ(value->Dims()[2], N); + CHECK_EQ(key->Dims()[3], d); + CHECK_EQ(value->Dims()[3], d); + CHECK_EQ(H_q % H_kv, 0) << "H_q must be divisible by H_kv for GQA"; + + // Compute scale + float scale = scale_.has_value() ? scale_.value() : (1.0f / std::sqrt(static_cast(d))); + + auto device = query->GetDevice().type(); + + // Call the fused FlashAttention forward kernel + // Returns: {output [B, H_q, N, d], logsumexp [B, H_q, N]} + auto results = Dispatcher::Instance().Call>>( + {device, "FlashAttentionForward"}, query, key, value, scale, is_causal_, dropout_p_); + + return results; +} + +void ScaledDotProductAttention::SetupContext(const std::vector> &input_tensors, + const std::vector> &output_tensors) { + // Save inputs and forward outputs needed for backward + // output_tensors[0] = O, output_tensors[1] = L (logsumexp) + saved_tensors_ = {input_tensors[0], input_tensors[1], input_tensors[2], output_tensors[0], output_tensors[1]}; +} + +std::vector> +ScaledDotProductAttention::Backward(const std::vector> &grad_outputs) { + CHECK_EQ(grad_outputs.size(), 1) << "Expected 1 gradient output (dO)"; + CHECK_EQ(saved_tensors_.size(), 5) << "Expected 5 saved tensors: Q, K, V, O, L"; + + const auto &query = saved_tensors_[0]; + const auto &key = saved_tensors_[1]; + const auto &value = saved_tensors_[2]; + const auto &output = saved_tensors_[3]; + const auto &logsumexp = saved_tensors_[4]; + const auto &grad_output = grad_outputs[0]; + + const auto d = query->Dims()[3]; + float scale = scale_.has_value() ? scale_.value() : (1.0f / std::sqrt(static_cast(d))); + + auto device = query->GetDevice().type(); + + // Call the fused FlashAttention backward kernel + // Returns: {dQ, dK, dV} + auto grads = Dispatcher::Instance().Call>>( + {device, "FlashAttentionBackward"}, grad_output, query, key, value, output, logsumexp, scale, is_causal_, + dropout_p_); + + return grads; +} + +} // namespace infini_train::autograd diff --git a/infini_train/src/kernels/cuda/scaled_dot_product_attention.cu b/infini_train/src/kernels/cuda/scaled_dot_product_attention.cu new file mode 100644 index 00000000..0e9ec4ab --- /dev/null +++ b/infini_train/src/kernels/cuda/scaled_dot_product_attention.cu @@ -0,0 +1,637 @@ +// FlashAttention v2 CUDA kernel implementation for InfiniTrain. +// +// Implements IO-aware fused attention with online softmax, supporting: +// - Forward and backward passes (full recomputation-based backward) +// - Causal masking +// - Configurable scaling factor +// - GQA (Grouped Query Attention): Q may have more heads than K/V +// - Dropout with deterministic Philox RNG +// +// Reference: FlashAttention-2 (Dao, 2023), arXiv:2307.08691 +// +// Data layout: Q, K, V in [B, H, N, d] (batch, head, sequence, head_dim) +// All intermediate computations are in float32 for numerical stability. + +#include +#include +#include +#include + +#include +#include + +#include "glog/logging.h" + +#include "infini_train/include/common/cuda/common_cuda.h" +#include "infini_train/include/common/cuda/kernel_helper.cuh" +#include "infini_train/include/core/runtime/device_guard.h" +#include "infini_train/include/dispatcher.h" +#include "infini_train/include/tensor.h" + +#include "infini_train/src/core/runtime/cuda/cuda_runtime_common.h" + +namespace infini_train::kernels::cuda { + +namespace { + +// Get the CUDA stream for the given device. +cudaStream_t GetCudaStream(const Device &device) { + return dynamic_cast( + infini_train::core::GetDeviceGuardImpl(device.type())->GetStream(device)) + ->cuda_stream(); +} + +// Philox-based deterministic RNG for dropout reproducibility. +// Given a 64-bit counter and a seed, produces a pseudo-random float in [0, 1). +__device__ __forceinline__ float philox_uniform(unsigned long long counter, unsigned long long seed) { + unsigned long long x = counter ^ seed; + x ^= x >> 33; + x *= 0xff51afd7ed558ccdULL; + x ^= x >> 33; + x *= 0xc4ceb9fe1a85ec53ULL; + x ^= x >> 33; + return (x & 0xFFFFFFFF) * 2.3283064365386963e-10f; +} + +// ============================================================================ +// FlashAttention Forward Kernel +// ============================================================================ +// +// Each thread block processes one (batch, q_head, q_tile) combination. +// It iterates over all K/V tiles, computing attention using online softmax. +// +// Shared memory layout (all float): +// sQ [Br * d] - query tile +// sKV [Bc * d] - key or value tile (reused: loads K first, then V) +// sS [Br * Bc] - attention scores / probabilities +// row_m [Br] - running row max +// row_l [Br] - running row sum +// sO [Br * d] - output accumulator +template +__global__ void FlashAttnFwdKernel(const T *__restrict__ Q, // [B, H_q, N, d] + const T *__restrict__ K, // [B, H_kv, N, d] + const T *__restrict__ V, // [B, H_kv, N, d] + T *__restrict__ O, // [B, H_q, N, d] + float *__restrict__ L, // [B, H_q, N] + int N, int d, int H_q, int H_kv, float scale, bool is_causal, float dropout_p, + unsigned long long rng_seed) { + const int q_tile_idx = blockIdx.x; + const int bh_idx = blockIdx.y; + const int batch_idx = bh_idx / H_q; + const int head_idx = bh_idx % H_q; + const int kv_head_idx = H_kv == H_q ? head_idx : head_idx / (H_q / H_kv); + const int tid = threadIdx.x; + const int num_threads = blockDim.x; + + const int q_start = q_tile_idx * Br; + if (q_start >= N) { + return; + } + const int q_len = min(Br, N - q_start); + + // Global memory pointers + const int64_t q_head_offset = ((int64_t)batch_idx * H_q + head_idx) * N * d; + const int64_t kv_head_offset = ((int64_t)batch_idx * H_kv + kv_head_idx) * N * d; + const T *Q_ptr = Q + q_head_offset + (int64_t)q_start * d; + T *O_ptr = O + q_head_offset + (int64_t)q_start * d; + float *L_ptr = L + ((int64_t)batch_idx * H_q + head_idx) * N + q_start; + const T *K_base = K + kv_head_offset; + const T *V_base = V + kv_head_offset; + + // Shared memory + extern __shared__ float smem[]; + float *sQ = smem; // [Br * d] + float *sKV = sQ + Br * d; // [Bc * d] (holds K then V) + float *sS = sKV + Bc * d; // [Br * Bc] + float *row_m = sS + Br * Bc; // [Br] + float *row_l = row_m + Br; // [Br] + float *sO = row_l + Br; // [Br * d] + + // Load Q tile (convert to float) + for (int idx = tid; idx < q_len * d; idx += num_threads) { + int r = idx / d; + int c = idx % d; + sQ[r * d + c] = common::cuda::Cast(Q_ptr[r * d + c]); + } + // Initialize accumulators + for (int idx = tid; idx < Br; idx += num_threads) { + row_m[idx] = -INFINITY; + row_l[idx] = 0.0f; + } + for (int idx = tid; idx < Br * d; idx += num_threads) { + sO[idx] = 0.0f; + } + __syncthreads(); + + // Iterate over KV tiles + const int num_kv_tiles = (N + Bc - 1) / Bc; + + for (int kv_tile = 0; kv_tile < num_kv_tiles; ++kv_tile) { + const int kv_start = kv_tile * Bc; + const int kv_len = min(Bc, N - kv_start); + + // Early exit for causal: skip if all KV positions are after all Q positions + if (is_causal && kv_start > q_start + q_len - 1) { + break; + } + + // --- Phase 1: Load K, compute S = Q @ K^T * scale --- + for (int idx = tid; idx < kv_len * d; idx += num_threads) { + int r = idx / d; + int c = idx % d; + sKV[r * d + c] = common::cuda::Cast(K_base[(kv_start + r) * d + c]); + } + __syncthreads(); + + // Compute S[qi][ki] = sum_c Q[qi][c] * K[ki][c] * scale + // Use consistent stride Bc for sS indexing + for (int idx = tid; idx < q_len * Bc; idx += num_threads) { + int qi = idx / Bc; + int ki = idx % Bc; + if (ki < kv_len) { + float dot = 0.0f; + for (int c = 0; c < d; ++c) { + dot += sQ[qi * d + c] * sKV[ki * d + c]; + } + dot *= scale; + // Apply causal mask + if (is_causal && (kv_start + ki) > (q_start + qi)) { + dot = -INFINITY; + } + sS[qi * Bc + ki] = dot; + } else { + sS[qi * Bc + ki] = -INFINITY; + } + } + __syncthreads(); + + // --- Phase 2: Online softmax per row --- + // Each thread handles one row: compute max, exp(S-max), row_sum, rescale + for (int qi = tid; qi < q_len; qi += num_threads) { + float m_old = row_m[qi]; + float l_old = row_l[qi]; + + // Find row max + float m_new = m_old; + for (int ki = 0; ki < kv_len; ++ki) { + m_new = fmaxf(m_new, sS[qi * Bc + ki]); + } + + // Compute P = exp(S - m_new) and row sum + float l_sum = 0.0f; + for (int ki = 0; ki < kv_len; ++ki) { + float s_val = sS[qi * Bc + ki]; + float p = (s_val > -INFINITY) ? expf(s_val - m_new) : 0.0f; + // Apply dropout + if (dropout_p > 0.0f && p > 0.0f) { + unsigned long long counter = (unsigned long long)(batch_idx * H_q + head_idx) * N * N + + (unsigned long long)(q_start + qi) * N + (kv_start + ki); + float r = philox_uniform(counter, rng_seed); + p = (r < dropout_p) ? 0.0f : p / (1.0f - dropout_p); + } + sS[qi * Bc + ki] = p; + l_sum += p; + } + // Zero out padding positions in P (already 0 from exp(-inf) but be explicit) + for (int ki = kv_len; ki < Bc; ++ki) { + sS[qi * Bc + ki] = 0.0f; + } + + // Rescale old output accumulator + float rescale = (m_old > -INFINITY) ? expf(m_old - m_new) : 0.0f; + for (int c = 0; c < d; ++c) { + sO[qi * d + c] *= rescale; + } + + row_m[qi] = m_new; + row_l[qi] = rescale * l_old + l_sum; + } + __syncthreads(); + + // --- Phase 3: Load V, accumulate P @ V --- + for (int idx = tid; idx < kv_len * d; idx += num_threads) { + int r = idx / d; + int c = idx % d; + sKV[r * d + c] = common::cuda::Cast(V_base[(kv_start + r) * d + c]); + } + __syncthreads(); + + // O[qi][c] += sum_ki P[qi][ki] * V[ki][c] + for (int idx = tid; idx < q_len * d; idx += num_threads) { + int qi = idx / d; + int c = idx % d; + float acc = 0.0f; + for (int ki = 0; ki < kv_len; ++ki) { + acc += sS[qi * Bc + ki] * sKV[ki * d + c]; + } + sO[qi * d + c] += acc; + } + __syncthreads(); + } + + // --- Phase 4: Normalize output and write --- + for (int idx = tid; idx < q_len * d; idx += num_threads) { + int qi = idx / d; + int c = idx % d; + float l_val = row_l[qi]; + float o_val = (l_val > 0.0f) ? sO[qi * d + c] / l_val : 0.0f; + O_ptr[qi * d + c] = common::cuda::Cast(o_val); + } + // Write logsumexp L = m + log(l) + for (int qi = tid; qi < q_len; qi += num_threads) { + L_ptr[qi] = (row_l[qi] > 0.0f) ? row_m[qi] + logf(row_l[qi]) : -INFINITY; + } +} + +// ============================================================================ +// FlashAttention Backward Kernel +// ============================================================================ +// +// Recomputation-based backward: recomputes attention weights from Q, K, V, L +// to avoid storing the N x N attention matrix. +// +// Uses float accumulators for dK, dV (written to float buffers). +// This avoids atomicAdd issues with bf16 and ensures numerical precision. +// +// Shared memory layout (all float): +// sQ [Br * d] - query tile +// sdO [Br * d] - dO tile +// sKV [Bc * d] - key or value tile (reused) +// sS [Br * Bc] - attention scores / P / dS (reused) +// sD [Br] - D = rowsum(dO * O) for each query row +// sdQ [Br * d] - dQ accumulator +// sL [Br] - logsumexp for each query row +template +__global__ void FlashAttnBwdKernel(const T *__restrict__ dO_global, // [B, H_q, N, d] + const T *__restrict__ Q, // [B, H_q, N, d] + const T *__restrict__ K, // [B, H_kv, N, d] + const T *__restrict__ V, // [B, H_kv, N, d] + const T *__restrict__ O, // [B, H_q, N, d] + const float *__restrict__ L, // [B, H_q, N] + float *__restrict__ dQ_global, // [B, H_q, N, d] (float) + float *__restrict__ dK_global, // [B, H_kv, N, d] (float) + float *__restrict__ dV_global, // [B, H_kv, N, d] (float) + int N, int d, int H_q, int H_kv, float scale, bool is_causal, float dropout_p, + unsigned long long rng_seed) { + const int q_tile_idx = blockIdx.x; + const int bh_idx = blockIdx.y; + const int batch_idx = bh_idx / H_q; + const int head_idx = bh_idx % H_q; + const int kv_head_idx = H_kv == H_q ? head_idx : head_idx / (H_q / H_kv); + const int tid = threadIdx.x; + const int num_threads = blockDim.x; + + const int q_start = q_tile_idx * Br; + if (q_start >= N) { + return; + } + const int q_len = min(Br, N - q_start); + + // Pointers + const int64_t q_head_offset = ((int64_t)batch_idx * H_q + head_idx) * N * d; + const int64_t kv_head_offset = ((int64_t)batch_idx * H_kv + kv_head_idx) * N * d; + const T *Q_ptr = Q + q_head_offset + (int64_t)q_start * d; + const T *dO_ptr = dO_global + q_head_offset + (int64_t)q_start * d; + const T *O_ptr = O + q_head_offset + (int64_t)q_start * d; + const float *L_ptr = L + ((int64_t)batch_idx * H_q + head_idx) * N + q_start; + float *dQ_out = dQ_global + q_head_offset + (int64_t)q_start * d; + const T *K_base = K + kv_head_offset; + const T *V_base = V + kv_head_offset; + float *dK_base = dK_global + kv_head_offset; + float *dV_base = dV_global + kv_head_offset; + + // Shared memory + extern __shared__ float smem[]; + float *sQ = smem; + float *sdO = sQ + Br * d; + float *sKV = sdO + Br * d; + float *sS = sKV + Bc * d; + float *sD = sS + Br * Bc; + float *sdQ = sD + Br; + float *sL = sdQ + Br * d; + + // Load Q and dO + for (int idx = tid; idx < q_len * d; idx += num_threads) { + int r = idx / d; + int c = idx % d; + sQ[r * d + c] = common::cuda::Cast(Q_ptr[r * d + c]); + sdO[r * d + c] = common::cuda::Cast(dO_ptr[r * d + c]); + } + // Load L (logsumexp) + for (int qi = tid; qi < q_len; qi += num_threads) { + sL[qi] = L_ptr[qi]; + } + // Compute D[qi] = sum_c dO[qi][c] * O[qi][c] + for (int qi = tid; qi < q_len; qi += num_threads) { + float d_val = 0.0f; + for (int c = 0; c < d; ++c) { + d_val += common::cuda::Cast(dO_ptr[qi * d + c]) * common::cuda::Cast(O_ptr[qi * d + c]); + } + sD[qi] = d_val; + } + // Initialize dQ accumulator + for (int idx = tid; idx < q_len * d; idx += num_threads) { + sdQ[idx] = 0.0f; + } + __syncthreads(); + + const int num_kv_tiles = (N + Bc - 1) / Bc; + + for (int kv_tile = 0; kv_tile < num_kv_tiles; ++kv_tile) { + const int kv_start = kv_tile * Bc; + const int kv_len = min(Bc, N - kv_start); + + if (is_causal && kv_start > q_start + q_len - 1) { + break; + } + + // --- Load K tile --- + for (int idx = tid; idx < kv_len * d; idx += num_threads) { + int r = idx / d; + int c = idx % d; + sKV[r * d + c] = common::cuda::Cast(K_base[(kv_start + r) * d + c]); + } + __syncthreads(); + + // --- Recompute S = Q @ K^T * scale, then P = exp(S - L) --- + for (int idx = tid; idx < q_len * Bc; idx += num_threads) { + int qi = idx / Bc; + int ki = idx % Bc; + if (ki < kv_len) { + float dot = 0.0f; + for (int c = 0; c < d; ++c) { + dot += sQ[qi * d + c] * sKV[ki * d + c]; + } + dot *= scale; + + if (is_causal && (kv_start + ki) > (q_start + qi)) { + sS[qi * Bc + ki] = 0.0f; + } else { + float p = expf(dot - sL[qi]); + if (dropout_p > 0.0f && p > 0.0f) { + unsigned long long counter = (unsigned long long)(batch_idx * H_q + head_idx) * N * N + + (unsigned long long)(q_start + qi) * N + (kv_start + ki); + float r = philox_uniform(counter, rng_seed); + p = (r < dropout_p) ? 0.0f : p / (1.0f - dropout_p); + } + sS[qi * Bc + ki] = p; + } + } else { + sS[qi * Bc + ki] = 0.0f; + } + } + __syncthreads(); + + // --- dV += P^T @ dO (before overwriting sKV with V) --- + // dV[ki][c] += sum_qi P[qi][ki] * dO[qi][c] + // Write to float buffer via atomicAdd (safe for GQA) + for (int idx = tid; idx < kv_len * d; idx += num_threads) { + int ki = idx / d; + int c = idx % d; + float acc = 0.0f; + for (int qi = 0; qi < q_len; ++qi) { + acc += sS[qi * Bc + ki] * sdO[qi * d + c]; + } + atomicAdd(&dV_base[(kv_start + ki) * d + c], acc); + } + __syncthreads(); + + // --- Load V tile into sKV (reuse space since K is no longer needed) --- + for (int idx = tid; idx < kv_len * d; idx += num_threads) { + int r = idx / d; + int c = idx % d; + sKV[r * d + c] = common::cuda::Cast(V_base[(kv_start + r) * d + c]); + } + __syncthreads(); + + // --- Compute dS = P * (dP - D), where dP[qi][ki] = sum_c dO[qi][c] * V[ki][c] --- + for (int idx = tid; idx < q_len * kv_len; idx += num_threads) { + int qi = idx / kv_len; + int ki = idx % kv_len; + float dp = 0.0f; + for (int c = 0; c < d; ++c) { + dp += sdO[qi * d + c] * sKV[ki * d + c]; + } + float p = sS[qi * Bc + ki]; + sS[qi * Bc + ki] = p * (dp - sD[qi]); // dS overwrites P + } + __syncthreads(); + + // --- Reload K tile for dQ and dK computation --- + for (int idx = tid; idx < kv_len * d; idx += num_threads) { + int r = idx / d; + int c = idx % d; + sKV[r * d + c] = common::cuda::Cast(K_base[(kv_start + r) * d + c]); + } + __syncthreads(); + + // dQ += dS @ K * scale + for (int idx = tid; idx < q_len * d; idx += num_threads) { + int qi = idx / d; + int c = idx % d; + float acc = 0.0f; + for (int ki = 0; ki < kv_len; ++ki) { + acc += sS[qi * Bc + ki] * sKV[ki * d + c]; + } + sdQ[qi * d + c] += acc * scale; + } + + // dK += dS^T @ Q * scale (atomicAdd to float buffer for GQA safety) + for (int idx = tid; idx < kv_len * d; idx += num_threads) { + int ki = idx / d; + int c = idx % d; + float acc = 0.0f; + for (int qi = 0; qi < q_len; ++qi) { + acc += sS[qi * Bc + ki] * sQ[qi * d + c]; + } + atomicAdd(&dK_base[(kv_start + ki) * d + c], acc * scale); + } + __syncthreads(); + } + + // Write dQ to float buffer + for (int idx = tid; idx < q_len * d; idx += num_threads) { + dQ_out[idx] = sdQ[idx]; + } +} + +// ============================================================================ +// Kernel to convert float gradient buffer to target dtype +// ============================================================================ +template +__global__ void ConvertFloatToType(const float *__restrict__ src, T *__restrict__ dst, int64_t n) { + int64_t idx = (int64_t)blockIdx.x * blockDim.x + threadIdx.x; + if (idx < n) { + dst[idx] = common::cuda::Cast(src[idx]); + } +} + +} // anonymous namespace + +// ============================================================================ +// Launch helpers +// ============================================================================ + +template +void LaunchFlashAttnForward(const std::shared_ptr &Q, const std::shared_ptr &K, + const std::shared_ptr &V, std::shared_ptr &O, std::shared_ptr &L, + float scale, bool is_causal, float dropout_p, cudaStream_t stream) { + const auto &dims = Q->Dims(); + const int B = dims[0]; + const int H_q = dims[1]; + const int N = dims[2]; + const int head_dim = dims[3]; + const int H_kv = K->Dims()[1]; + + constexpr int Br = 32; + constexpr int Bc = 32; + constexpr int NUM_THREADS = 128; + + // Shared memory: sQ[Br*d] + sKV[Bc*d] + sS[Br*Bc] + row_m[Br] + row_l[Br] + sO[Br*d] + size_t smem_size = (size_t)(Br * head_dim + Bc * head_dim + Br * Bc + Br + Br + Br * head_dim) * sizeof(float); + + dim3 grid((N + Br - 1) / Br, B * H_q); + dim3 block(NUM_THREADS); + + unsigned long long rng_seed = 42; + + FlashAttnFwdKernel<<>>( + static_cast(Q->DataPtr()), static_cast(K->DataPtr()), + static_cast(V->DataPtr()), static_cast(O->DataPtr()), static_cast(L->DataPtr()), N, + head_dim, H_q, H_kv, scale, is_causal, dropout_p, rng_seed); +} + +template +void LaunchFlashAttnBackward(const std::shared_ptr &dO, const std::shared_ptr &Q, + const std::shared_ptr &K, const std::shared_ptr &V, + const std::shared_ptr &O, const std::shared_ptr &L, + std::shared_ptr &dQ, std::shared_ptr &dK, std::shared_ptr &dV, + float scale, bool is_causal, float dropout_p, cudaStream_t stream) { + const auto &dims = Q->Dims(); + const int B = dims[0]; + const int H_q = dims[1]; + const int N = dims[2]; + const int head_dim = dims[3]; + const int H_kv = K->Dims()[1]; + + constexpr int Br = 32; + constexpr int Bc = 32; + constexpr int NUM_THREADS = 128; + + // Shared memory: sQ[Br*d] + sdO[Br*d] + sKV[Bc*d] + sS[Br*Bc] + sD[Br] + sdQ[Br*d] + sL[Br] + size_t smem_size + = (size_t)(Br * head_dim * 2 + Bc * head_dim + Br * Bc + Br + Br * head_dim + Br) * sizeof(float); + + dim3 grid((N + Br - 1) / Br, B * H_q); + dim3 block(NUM_THREADS); + + unsigned long long rng_seed = 42; + + // Allocate float buffers for gradient accumulation (required for atomicAdd with GQA + bf16) + auto dQ_float = std::make_shared(Q->Dims(), DataType::kFLOAT32, Q->GetDevice()); + auto dK_float = std::make_shared(K->Dims(), DataType::kFLOAT32, K->GetDevice()); + auto dV_float = std::make_shared(V->Dims(), DataType::kFLOAT32, V->GetDevice()); + + cudaMemsetAsync(dQ_float->DataPtr(), 0, dQ_float->NumElements() * sizeof(float), stream); + cudaMemsetAsync(dK_float->DataPtr(), 0, dK_float->NumElements() * sizeof(float), stream); + cudaMemsetAsync(dV_float->DataPtr(), 0, dV_float->NumElements() * sizeof(float), stream); + + FlashAttnBwdKernel<<>>( + static_cast(dO->DataPtr()), static_cast(Q->DataPtr()), + static_cast(K->DataPtr()), static_cast(V->DataPtr()), + static_cast(O->DataPtr()), static_cast(L->DataPtr()), + static_cast(dQ_float->DataPtr()), static_cast(dK_float->DataPtr()), + static_cast(dV_float->DataPtr()), N, head_dim, H_q, H_kv, scale, is_causal, dropout_p, rng_seed); + + // Convert float gradients to target dtype + if constexpr (std::is_same_v) { + // Already float: just copy the data + cudaMemcpyAsync(dQ->DataPtr(), dQ_float->DataPtr(), dQ_float->NumElements() * sizeof(float), + cudaMemcpyDeviceToDevice, stream); + cudaMemcpyAsync(dK->DataPtr(), dK_float->DataPtr(), dK_float->NumElements() * sizeof(float), + cudaMemcpyDeviceToDevice, stream); + cudaMemcpyAsync(dV->DataPtr(), dV_float->DataPtr(), dV_float->NumElements() * sizeof(float), + cudaMemcpyDeviceToDevice, stream); + } else { + // Convert float -> T (e.g., bf16) + constexpr int kConvertThreads = 256; + int64_t nQ = dQ_float->NumElements(); + int64_t nK = dK_float->NumElements(); + int64_t nV = dV_float->NumElements(); + + ConvertFloatToType<<<(nQ + kConvertThreads - 1) / kConvertThreads, kConvertThreads, 0, stream>>>( + static_cast(dQ_float->DataPtr()), static_cast(dQ->DataPtr()), nQ); + ConvertFloatToType<<<(nK + kConvertThreads - 1) / kConvertThreads, kConvertThreads, 0, stream>>>( + static_cast(dK_float->DataPtr()), static_cast(dK->DataPtr()), nK); + ConvertFloatToType<<<(nV + kConvertThreads - 1) / kConvertThreads, kConvertThreads, 0, stream>>>( + static_cast(dV_float->DataPtr()), static_cast(dV->DataPtr()), nV); + } +} + +// ============================================================================ +// Dispatcher-registered functions +// ============================================================================ + +std::vector> FlashAttentionForward(const std::shared_ptr &query, + const std::shared_ptr &key, + const std::shared_ptr &value, float scale, + bool is_causal, float dropout_p) { + const auto &dims = query->Dims(); + auto dtype = query->Dtype(); + auto device = query->GetDevice(); + + auto output = std::make_shared(dims, dtype, device); + auto logsumexp + = std::make_shared(std::vector{dims[0], dims[1], dims[2]}, DataType::kFLOAT32, device); + + auto stream = GetCudaStream(device); + + switch (dtype) { + DISPATCH_CASE(WRAP(LaunchFlashAttnForward(query, key, value, output, logsumexp, scale, is_causal, + dropout_p, stream);), + DataType::kFLOAT32) + DISPATCH_CASE(WRAP(LaunchFlashAttnForward(query, key, value, output, logsumexp, scale, is_causal, + dropout_p, stream);), + DataType::kBFLOAT16) + default: + LOG(FATAL) << "FlashAttention forward: unsupported dtype"; + } + + return {output, logsumexp}; +} + +std::vector> +FlashAttentionBackward(const std::shared_ptr &grad_output, const std::shared_ptr &query, + const std::shared_ptr &key, const std::shared_ptr &value, + const std::shared_ptr &output, const std::shared_ptr &logsumexp, float scale, + bool is_causal, float dropout_p) { + auto dtype = query->Dtype(); + auto device = query->GetDevice(); + + auto dQ = std::make_shared(query->Dims(), dtype, device); + auto dK = std::make_shared(key->Dims(), dtype, device); + auto dV = std::make_shared(value->Dims(), dtype, device); + + auto stream = GetCudaStream(device); + + switch (dtype) { + DISPATCH_CASE(WRAP(LaunchFlashAttnBackward(grad_output, query, key, value, output, logsumexp, dQ, dK, dV, + scale, is_causal, dropout_p, stream);), + DataType::kFLOAT32) + DISPATCH_CASE(WRAP(LaunchFlashAttnBackward(grad_output, query, key, value, output, logsumexp, dQ, + dK, dV, scale, is_causal, dropout_p, stream);), + DataType::kBFLOAT16) + default: + LOG(FATAL) << "FlashAttention backward: unsupported dtype"; + } + + return {dQ, dK, dV}; +} + +} // namespace infini_train::kernels::cuda + +// Register kernels with the dispatcher +REGISTER_KERNEL(infini_train::Device::DeviceType::kCUDA, FlashAttentionForward, + infini_train::kernels::cuda::FlashAttentionForward) +REGISTER_KERNEL(infini_train::Device::DeviceType::kCUDA, FlashAttentionBackward, + infini_train::kernels::cuda::FlashAttentionBackward) diff --git a/infini_train/src/nn/functional.cc b/infini_train/src/nn/functional.cc index b02f185a..c4131650 100644 --- a/infini_train/src/nn/functional.cc +++ b/infini_train/src/nn/functional.cc @@ -8,6 +8,7 @@ #include "infini_train/include/autograd/elementwise.h" #include "infini_train/include/autograd/misc.h" #include "infini_train/include/autograd/reduction.h" +#include "infini_train/include/autograd/scaled_dot_product_attention.h" #include "infini_train/include/autograd/softmax.h" #include "infini_train/include/autograd/transform.h" #include "infini_train/include/nn/init.h" @@ -79,4 +80,12 @@ std::shared_ptr Softmax(const std::shared_ptr &input, int64_t di std::shared_ptr Sigmoid(const std::shared_ptr &input) { return std::make_shared()->Apply({input})[0]; } + +std::shared_ptr ScaledDotProductAttention(const std::shared_ptr &query, + const std::shared_ptr &key, + const std::shared_ptr &value, bool is_causal, + float dropout_p, std::optional scale) { + return std::make_shared(is_causal, dropout_p, scale) + ->Apply({query, key, value})[0]; +} } // namespace infini_train::nn::function From 13be50e6cdb1f9c4cd065979112559faa4565a70 Mon Sep 17 00:00:00 2001 From: LiaoYFBH <2273398935@qq.com> Date: Mon, 9 Mar 2026 21:10:46 +0800 Subject: [PATCH 02/13] docs: add FlashAttention design document and test config - docs/flash_attention_design.md: Complete design document covering algorithm, architecture, kernel design, GQA, API, and performance - scripts/test_config_flash.json: Test config for run_models_and_profile.bash with 8 test cases: baseline vs flash x {fp32,bf16} x {seq64,256,512} Verified on remote A100: all 5 configurations (GPT-2 fp32/bf16, LLaMA-3 fp32, baseline/flash) run successfully with matching loss values. --- docs/flash_attention_design.md | 393 +++++++++++++++++++++++++++++++++ scripts/test_config_flash.json | 113 ++++++++++ 2 files changed, 506 insertions(+) create mode 100644 docs/flash_attention_design.md create mode 100644 scripts/test_config_flash.json diff --git a/docs/flash_attention_design.md b/docs/flash_attention_design.md new file mode 100644 index 00000000..fa8bd7ee --- /dev/null +++ b/docs/flash_attention_design.md @@ -0,0 +1,393 @@ +# FlashAttention 接入设计文档 + +## 1. 概述 + +### 1.1 任务目标 + +在 InfiniTrain 框架中实现 FlashAttention v2 算法的完整接入，包括： + +- 手写 FlashAttention CUDA kernel（前向 + 反向传播） +- 支持 causal mask、可配置 scale、dropout、GQA +- 集成到框架的 Autograd 和 Dispatcher 系统 +- 在 GPT-2 和 LLaMA-3 模型中通过 `--flash` 命令行开关启用 + +### 1.2 算法原理 + +FlashAttention v2 的核心思想是通过 **IO-aware tiling** 将注意力计算分块执行，避免显式构造 $N \times N$ 的注意力矩阵。其关键技术包括： + +1. **分块计算 (Tiling)**：将 Q 分成大小为 $B_r$ 的块，K/V 分成大小为 $B_c$ 的块 +2. **在线 Softmax (Online Softmax)**：使用 running max 和 running sum 避免两遍扫描 +3. **重计算 (Recomputation)**：反向传播时重新计算注意力权重 $P$，避免存储 $O(N^2)$ 中间结果 +4. **数值稳定性**：所有中间计算使用 float32 + +标准注意力的复杂度： +$$\text{memory: } O(N^2), \quad \text{IO: } O(N^2 d)$$ + +FlashAttention 的复杂度： +$$\text{memory: } O(N), \quad \text{IO: } O(N^2 d^2 / M)$$ + +其中 $M$ 是 SRAM（shared memory）大小。 + +### 1.3 参考文献 + +- Dao, T. (2023). FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning. arXiv:2307.08691 + +## 2. 架构设计 + +### 2.1 整体架构 + +``` +用户代码 (GPT-2/LLaMA-3) + │ nn::function::ScaledDotProductAttention(Q, K, V, is_causal=true) + ▼ +nn::functional 层 + │ 创建 autograd::ScaledDotProductAttention Function + │ 调用 Apply({Q, K, V}) + ▼ +Autograd 层 (ScaledDotProductAttention) + │ Forward: Dispatcher -> "FlashAttentionForward" + │ SetupContext: 保存 {Q, K, V, O, L} + │ Backward: Dispatcher -> "FlashAttentionBackward" + ▼ +CUDA Kernel 层 (scaled_dot_product_attention.cu) + │ FlashAttnFwdKernel - 分块在线 softmax + P@V + │ FlashAttnBwdKernel - 重计算 + dQ/dK/dV + ▼ +Dispatcher 注册 + REGISTER_KERNEL(kCUDA, FlashAttentionForward, ...) + REGISTER_KERNEL(kCUDA, FlashAttentionBackward, ...) +``` + +### 2.2 文件结构 + +``` +新增文件： + infini_train/include/autograd/scaled_dot_product_attention.h # Autograd Function 声明 + infini_train/src/autograd/scaled_dot_product_attention.cc # Autograd 实现 + infini_train/src/kernels/cuda/scaled_dot_product_attention.cu # CUDA kernel + +修改文件： + infini_train/include/nn/functional.h # 添加 ScaledDotProductAttention 接口 + infini_train/src/nn/functional.cc # 添加实现 + example/gpt2/main.cc # 添加 --flash flag + example/gpt2/net.h # GPT2Config 添加 flash 字段 + example/gpt2/net.cc # 注意力前向添加 flash 分支 + example/llama3/main.cc # 添加 --flash flag + example/llama3/net.h # FromLLMC 接口变更 + example/llama3/net.cc # 注意力前向添加 flash 分支（含 GQA） +``` + +### 2.3 设计原则 + +1. **最小侵入性**：通过新增文件实现核心功能，对现有代码修改最小化 +2. **API 兼容性**：接口对齐 PyTorch `F.scaled_dot_product_attention` +3. **框架一致性**：遵循 InfiniTrain 的 Dispatcher + Autograd + REGISTER_KERNEL 模式 +4. **类型安全**：支持 float32 和 bfloat16，backward 使用 float32 累积保证精度 + +## 3. 详细设计 + +### 3.1 CUDA Kernel 设计 + +#### 3.1.1 前向 Kernel + +**核心算法**：带有在线 Softmax 的分块注意力计算。 + +``` +输入: Q [B, H_q, N, d], K [B, H_kv, N, d], V [B, H_kv, N, d] +输出: O [B, H_q, N, d], L [B, H_q, N] (logsumexp) + +对每个 (batch, q_head, q_tile) 分配一个 thread block: + 将 Q 的对应 tile 加载到 shared memory: sQ [Br × d] + 初始化: row_m = -inf, row_l = 0, sO = 0 + + FOR 每个 KV tile: + 加载 K tile 到 sKV [Bc × d] + 计算 S = sQ @ sKV^T × scale [Br × Bc] + 应用 causal mask（如启用） + + 在线 softmax 更新: + m_new = max(row_m, rowmax(S)) + P = exp(S - m_new) + rescale = exp(row_m - m_new) + sO = rescale × sO + row_l = rescale × row_l + rowsum(P) + row_m = m_new + + 加载 V tile 到 sKV [Bc × d] + sO += P @ sKV + + 归一化: O = sO / row_l + 写回: L = row_m + log(row_l) +``` + +**Shared Memory 布局**： +| 区域 | 大小 | 用途 | +|------|------|------| +| sQ | Br × d | Query tile (float) | +| sKV | Bc × d | Key/Value tile (复用) | +| sS | Br × Bc | 注意力分数 / 概率 | +| row_m | Br | 行最大值 | +| row_l | Br | 行求和 | +| sO | Br × d | 输出累积器 | + +**总计**: $(2 B_r d + B_c d + B_r B_c + 2 B_r) \times 4$ bytes + +#### 3.1.2 反向 Kernel + +**核心算法**：基于重计算的反向传播，避免存储 $N \times N$ 注意力矩阵。 + +``` +输入: dO, Q, K, V, O, L (logsumexp) +输出: dQ [float], dK [float], dV [float] + +预计算: D[qi] = sum_c dO[qi][c] × O[qi][c] + +对每个 (batch, q_head, q_tile): + 加载 Q, dO tile 到 shared memory + 初始化 dQ accumulator = 0 + + FOR 每个 KV tile: + 加载 K tile + 重计算: S = Q @ K^T × scale + 重计算: P = exp(S - L) (含 causal mask, dropout) + + dV += P^T @ dO (atomicAdd 到 float buffer) + + 加载 V tile + dP = dO @ V^T + dS = P × (dP - D) + + 重新加载 K tile + dQ += dS @ K × scale + dK += dS^T @ Q × scale (atomicAdd 到 float buffer) + + 写回 dQ +``` + +**关键设计决策**： + +1. **Float 梯度缓冲区**：dK、dV 使用 float32 全局缓冲区 + atomicAdd，确保 GQA 场景多个 Q head 映射到同一 KV head 时的正确性，同时避免 bf16 atomicAdd 不可用的问题。 +2. **类型转换 Kernel**：反向完成后，使用 `ConvertFloatToType` kernel 将 float32 梯度转换为目标类型 (如 bf16)。 + +#### 3.1.3 GQA 支持 + +Grouped Query Attention 通过 head 映射实现： +```cpp +kv_head_idx = H_kv == H_q ? head_idx : head_idx / (H_q / H_kv); +``` + +- 前向：多个 Q head 共享同一 KV head，直接读取对应的 K/V +- 反向：多个 Q head 的梯度通过 atomicAdd 累积到同一 KV head 的 dK/dV + +### 3.2 Autograd Function + +`ScaledDotProductAttention` 继承 `autograd::Function`： + +- **Forward**: 校验输入维度，计算 scale，通过 Dispatcher 调用 CUDA kernel +- **SetupContext**: 保存 {Q, K, V, O, L} 共 5 个张量用于反向计算 +- **Backward**: 通过 Dispatcher 调用反向 CUDA kernel，返回 {dQ, dK, dV} + +### 3.3 Functional API + +```cpp +std::shared_ptr ScaledDotProductAttention( + const std::shared_ptr &query, // [B, H_q, N, d] + const std::shared_ptr &key, // [B, H_kv, N, d] + const std::shared_ptr &value, // [B, H_kv, N, d] + bool is_causal = false, + float dropout_p = 0.0f, + std::optional scale = std::nullopt); +``` + +### 3.4 模型集成 + +#### GPT-2 (MHA) + +```cpp +if (config_.flash) { + // Q, K, V 已经是 [B, h, T, d] 布局 + y = nn::function::ScaledDotProductAttention(q, k, v, /*is_causal=*/true); +} else { + // 原始小算子路径: matmul -> mask -> softmax -> matmul +} +``` + +#### LLaMA-3 (GQA) + +```cpp +if (config_.flash) { + // FlashAttention 原生支持 GQA，无需 RepeatKV + q = q->Transpose(1, 2); // [B, H_local, T, D] + k = k->Transpose(1, 2); // [B, KV_local, T, D] + v = v->Transpose(1, 2); + y = nn::function::ScaledDotProductAttention(q, k, v, /*is_causal=*/true); +} else { + k = RepeatKV(k, n_rep_); // 展开 KV heads + v = RepeatKV(v, n_rep_); + // 原始路径... +} +``` + +LLaMA-3 的 FlashAttention 路径跳过了 RepeatKV 操作，既节省了显存（避免复制 KV），又避免了额外的 transpose 开销。 + +## 4. Kernel 参数配置 + +| 参数 | 值 | 说明 | +|------|----|------| +| Br (Query Tile) | 32 | Query 维度分块大小 | +| Bc (KV Tile) | 32 | Key/Value 维度分块大小 | +| NUM_THREADS | 128 | 每个 thread block 的线程数 | +| 支持类型 | float32, bfloat16 | 通过模板特化 | +| 支持 head_dim | 任意 | 运行时参数 | +| CUDA Arch | sm_75, sm_80, sm_90 | A100 主要使用 sm_80 | + +## 5. 性能评估报告 + +### 5.1 实验环境 + +**硬件环境** + +| 项目 | 规格 | +|------|------| +| GPU | NVIDIA A100-SXM4-80GB × 8 | +| GPU 显存 | 80 GB HBM2e | +| CPU | 64 cores | +| 内存 | 512 GB | + +**软件环境** + +| 项目 | 版本 | +|------|------| +| OS | Ubuntu 24.04 LTS | +| CUDA | 12.8 | +| CUDA Driver | 570.86.15 | +| 编译器 | GCC 13 + NVCC 12.8 | +| CMake | 3.31.4 | +| 构建选项 | `-DUSE_CUDA=ON -DUSE_NCCL=ON` | + +### 5.2 实验配置 + +| 参数 | GPT-2 124M | LLaMA-3.2 1B | +|------|-----------|---------------| +| 模型参数量 | 124M | 1.24B | +| n_head / n_kv_head | 12 / 12 (MHA) | 32 / 8 (GQA) | +| head_dim | 64 | 64 | +| batch_size | 4 | 4 | +| sequence_length | 256 | 256 | +| dtype | float32 | float32 | +| 迭代次数 | 20 | 10 | +| overfit_single_batch | false | false | + +### 5.3 GPT-2 性能对比 + +| 指标 | Baseline (小算子拼接) | FlashAttention | 加速比/变化 | +|------|----------------------|----------------|-------------| +| 每步平均耗时 | ~174 ms | ~126 ms | **1.38× (Speedup)** | +| 吞吐率 (tokens/s) | ~5,880 | ~8,100 | **+37.8%** | +| GPU 显存占用 (峰值) | ~3,936 MB (稳定) | ~7,223 MB (初始) | +83.5% | +| Step 20 Loss | 4.063 | 4.097 | ΔLoss < 1% | + +**分析**： +- **速度提升**：FlashAttention 将注意力计算的多次全局内存读写融合为分块 shared memory 操作，减少了 HBM 带宽瓶颈，实现 1.38× 加速。 +- **显存变化**：当前实现在反向传播中分配临时 float32 梯度缓冲区用于 atomicAdd（保证 GQA + bf16 的正确性），导致显存占用高于 baseline。这是计算正确性与内存的权衡，可通过内存池预分配优化。 +- **正确性**：Flash 路径与 baseline 在相同初始权重下 Loss 差异 < 1%，数值精度对齐。 + +### 5.4 LLaMA-3.2 1B 性能对比 + +| 指标 | Baseline (小算子拼接) | FlashAttention | 说明 | +|------|----------------------|----------------|------| +| 训练 Loss 变化 | 4.90 → 4.21 (10步) | 4.90 → 4.21 (10步) | 收敛一致 | +| 吞吐率 (tokens/s) | ~1,100 | ~1,300 | +18% | +| GQA 支持 | RepeatKV 展开 | 原生 kernel 内处理 | 节省 KV 复制开销 | + +**分析**： +- LLaMA-3 使用 GQA (H_q=32, H_kv=8, n_rep=4)，FlashAttention 在 kernel 内通过 head 映射原生处理 GQA，无需调用 RepeatKV 展开 K/V，节省了一次完整的 KV tensor 复制。 +- 反向传播中多个 Q head 的梯度正确地通过 atomicAdd 累积到对应的 KV head。 + +### 5.5 正确性验证 + +| 模型 | 验证方法 | 结果 | +|------|---------|------| +| GPT-2 (MHA) | 相同权重、相同数据，对比 step 20 的 loss | Flash: 4.097 vs Baseline: 4.063, ΔLoss < 1% | +| LLaMA-3 (GQA) | 相同权重、相同数据，对比 loss 曲线 | 收敛趋势一致，4.90 → 4.21 | + +结论：FlashAttention 与原始小算子拼接版本在训练精度上对齐，浮点差异在可接受范围内。 + +## 6. 已知限制与改进方向 + +### 6.1 当前限制 + +1. **Shared Memory 受限**：使用 float32 shared memory 限制了可处理的 head_dim 大小 +2. **反向传播内存**：每次反向调用分配临时 float32 梯度缓冲区 +3. **Tiling 粒度**：Br=Bc=32 固定配置，未针对不同 head_dim 进行自适应调优 + +### 6.2 未来改进 + +1. **Register Tiling**：将部分 shared memory 数据提升到寄存器，提高计算密度 +2. **Warp-level Primitives**：使用 `__shfl_*` 指令加速归约操作 +3. **自适应 Tile Size**：根据 head_dim 和 GPU SM 数量动态选择 Br, Bc +4. **Tensor Core 加速**：利用 WMMA 指令在 A100 的 Tensor Core 上执行矩阵乘法 +5. **内存池**：预分配反向传播的 float32 缓冲区避免重复分配 + +## 7. 使用方式 + +### 7.1 编译 + +```bash +mkdir -p build && cd build +cmake -DUSE_CUDA=ON -DUSE_NCCL=ON .. +make -j$(nproc) +``` + +### 7.2 手动运行 + +```bash +# GPT-2 with FlashAttention +./gpt2 \ + --llmc_filepath= \ + --input_bin= \ + --flash \ + --batch_size=4 --sequence_length=256 + +# LLaMA-3 with FlashAttention (含 GQA) +./llama3 \ + --llmc_filepath= \ + --input_bin= \ + --flash \ + --batch_size=4 --sequence_length=256 +``` + +不传 `--flash` 即走原始小算子路径，行为完全不变。 + +### 7.3 完整运行脚本（端到端验证） + +使用提供的 `test_config_flash.json` 配合已有的 `run_models_and_profile.bash` 一键运行所有对比实验： + +```bash +# 在 scripts/ 目录下执行 +cd scripts +bash run_models_and_profile.bash test_config_flash.json +``` + +该脚本会自动： +1. 编译项目 +2. 依次运行 baseline（无 flash）和 flash 版本的 GPT-2 和 LLaMA-3 实验 +3. 覆盖多种配置：float32 / bfloat16，seq_len = 64 / 256 / 512 +4. 所有日志保存到 `logs_flash/` 目录下 + +`test_config_flash.json` 中定义了如下测试对： + +| 测试 ID | dtype | seq_len | batch | flash | 说明 | +|---------|-------|---------|-------|-------|------| +| baseline_fp32_seq64 | float32 | 64 | 4 | ✗ | 短序列基线 | +| flash_fp32_seq64 | float32 | 64 | 4 | ✓ | 短序列 flash | +| baseline_fp32_seq256 | float32 | 256 | 4 | ✗ | 中等序列基线 | +| flash_fp32_seq256 | float32 | 256 | 4 | ✓ | 中等序列 flash | +| baseline_fp32_seq512 | float32 | 512 | 2 | ✗ | 长序列基线 | +| flash_fp32_seq512 | float32 | 512 | 2 | ✓ | 长序列 flash | +| baseline_bf16_seq256 | bfloat16 | 256 | 4 | ✗ | bf16 基线 | +| flash_bf16_seq256 | bfloat16 | 256 | 4 | ✓ | bf16 flash | + +**注意**：运行前需根据实际环境修改 `test_config_flash.json` 中的数据路径变量： +- `GPT2_INPUT_BIN`、`GPT2_LLMC_FILEPATH` +- `LLAMA3_INPUT_BIN`、`LLAMA3_LLMC_FILEPATH` diff --git a/scripts/test_config_flash.json b/scripts/test_config_flash.json new file mode 100644 index 00000000..bf332ebf --- /dev/null +++ b/scripts/test_config_flash.json @@ -0,0 +1,113 @@ +{ + "variables": { + "BUILD_DIR": "../build", + "GPT2_INPUT_BIN": "/data/shared/InfiniTrain-dev/data/llmc/gpt2/tinyshakespeare/tiny_shakespeare_train.bin", + "GPT2_LLMC_FILEPATH": "/data/shared/InfiniTrain-dev/data/llmc/gpt2/gpt2_124M.bin", + "LLAMA3_INPUT_BIN": "/data/shared/InfiniTrain-dev/data/llmc/llama3/tinyshakespeare/tiny_shakespeare_train.bin", + "LLAMA3_LLMC_FILEPATH": "/data/shared/InfiniTrain-dev/data/llmc/llama3/llama3.2_1B_fp32.bin", + "PROFILE_LOG_DIR": "./profile_logs", + "LOG_DIR": "./logs_flash", + "COMPARE_LOG_DIR": "" + }, + "builds": [ + { + "id": "build_flash", + "profile": false, + "cmd": "cmake -DUSE_CUDA=ON -DUSE_NCCL=ON .. && make -j" + } + ], + "tests": [ + { + "id": "baseline_fp32_seq64", + "args": { + "dtype": "float32", + "num_iteration": 20, + "batch_size": 4, + "sequence_length": 64, + "total_batch_size": 256, + "overfit_single_batch": false + } + }, + { + "id": "flash_fp32_seq64", + "args": { + "dtype": "float32", + "num_iteration": 20, + "batch_size": 4, + "sequence_length": 64, + "total_batch_size": 256, + "overfit_single_batch": false, + "flash": true + } + }, + { + "id": "baseline_fp32_seq256", + "args": { + "dtype": "float32", + "num_iteration": 20, + "batch_size": 4, + "sequence_length": 256, + "total_batch_size": 1024, + "overfit_single_batch": false + } + }, + { + "id": "flash_fp32_seq256", + "args": { + "dtype": "float32", + "num_iteration": 20, + "batch_size": 4, + "sequence_length": 256, + "total_batch_size": 1024, + "overfit_single_batch": false, + "flash": true + } + }, + { + "id": "baseline_fp32_seq512", + "args": { + "dtype": "float32", + "num_iteration": 20, + "batch_size": 2, + "sequence_length": 512, + "total_batch_size": 1024, + "overfit_single_batch": false + } + }, + { + "id": "flash_fp32_seq512", + "args": { + "dtype": "float32", + "num_iteration": 20, + "batch_size": 2, + "sequence_length": 512, + "total_batch_size": 1024, + "overfit_single_batch": false, + "flash": true + } + }, + { + "id": "baseline_bf16_seq256", + "args": { + "dtype": "bfloat16", + "num_iteration": 20, + "batch_size": 4, + "sequence_length": 256, + "total_batch_size": 1024, + "overfit_single_batch": false + } + }, + { + "id": "flash_bf16_seq256", + "args": { + "dtype": "bfloat16", + "num_iteration": 20, + "batch_size": 4, + "sequence_length": 256, + "total_batch_size": 1024, + "overfit_single_batch": false, + "flash": true + } + } + ] +} From 6836634b95021083b34306092866f45dd4f2fc43 Mon Sep 17 00:00:00 2001 From: LiaoYFBH <2273398935@qq.com> Date: Mon, 9 Mar 2026 21:13:58 +0800 Subject: [PATCH 03/13] Remove docs/flash attention design.md from repository --- docs/flash_attention_design.md | 393 --------------------------------- 1 file changed, 393 deletions(-) delete mode 100644 docs/flash_attention_design.md diff --git a/docs/flash_attention_design.md b/docs/flash_attention_design.md deleted file mode 100644 index fa8bd7ee..00000000 --- a/docs/flash_attention_design.md +++ /dev/null @@ -1,393 +0,0 @@ -# FlashAttention 接入设计文档 - -## 1. 概述 - -### 1.1 任务目标 - -在 InfiniTrain 框架中实现 FlashAttention v2 算法的完整接入，包括： - -- 手写 FlashAttention CUDA kernel（前向 + 反向传播） -- 支持 causal mask、可配置 scale、dropout、GQA -- 集成到框架的 Autograd 和 Dispatcher 系统 -- 在 GPT-2 和 LLaMA-3 模型中通过 `--flash` 命令行开关启用 - -### 1.2 算法原理 - -FlashAttention v2 的核心思想是通过 **IO-aware tiling** 将注意力计算分块执行，避免显式构造 $N \times N$ 的注意力矩阵。其关键技术包括： - -1. **分块计算 (Tiling)**：将 Q 分成大小为 $B_r$ 的块，K/V 分成大小为 $B_c$ 的块 -2. **在线 Softmax (Online Softmax)**：使用 running max 和 running sum 避免两遍扫描 -3. **重计算 (Recomputation)**：反向传播时重新计算注意力权重 $P$，避免存储 $O(N^2)$ 中间结果 -4. **数值稳定性**：所有中间计算使用 float32 - -标准注意力的复杂度： -$$\text{memory: } O(N^2), \quad \text{IO: } O(N^2 d)$$ - -FlashAttention 的复杂度： -$$\text{memory: } O(N), \quad \text{IO: } O(N^2 d^2 / M)$$ - -其中 $M$ 是 SRAM（shared memory）大小。 - -### 1.3 参考文献 - -- Dao, T. (2023). FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning. arXiv:2307.08691 - -## 2. 架构设计 - -### 2.1 整体架构 - -``` -用户代码 (GPT-2/LLaMA-3) - │ nn::function::ScaledDotProductAttention(Q, K, V, is_causal=true) - ▼ -nn::functional 层 - │ 创建 autograd::ScaledDotProductAttention Function - │ 调用 Apply({Q, K, V}) - ▼ -Autograd 层 (ScaledDotProductAttention) - │ Forward: Dispatcher -> "FlashAttentionForward" - │ SetupContext: 保存 {Q, K, V, O, L} - │ Backward: Dispatcher -> "FlashAttentionBackward" - ▼ -CUDA Kernel 层 (scaled_dot_product_attention.cu) - │ FlashAttnFwdKernel - 分块在线 softmax + P@V - │ FlashAttnBwdKernel - 重计算 + dQ/dK/dV - ▼ -Dispatcher 注册 - REGISTER_KERNEL(kCUDA, FlashAttentionForward, ...) - REGISTER_KERNEL(kCUDA, FlashAttentionBackward, ...) -``` - -### 2.2 文件结构 - -``` -新增文件： - infini_train/include/autograd/scaled_dot_product_attention.h # Autograd Function 声明 - infini_train/src/autograd/scaled_dot_product_attention.cc # Autograd 实现 - infini_train/src/kernels/cuda/scaled_dot_product_attention.cu # CUDA kernel - -修改文件： - infini_train/include/nn/functional.h # 添加 ScaledDotProductAttention 接口 - infini_train/src/nn/functional.cc # 添加实现 - example/gpt2/main.cc # 添加 --flash flag - example/gpt2/net.h # GPT2Config 添加 flash 字段 - example/gpt2/net.cc # 注意力前向添加 flash 分支 - example/llama3/main.cc # 添加 --flash flag - example/llama3/net.h # FromLLMC 接口变更 - example/llama3/net.cc # 注意力前向添加 flash 分支（含 GQA） -``` - -### 2.3 设计原则 - -1. **最小侵入性**：通过新增文件实现核心功能，对现有代码修改最小化 -2. **API 兼容性**：接口对齐 PyTorch `F.scaled_dot_product_attention` -3. **框架一致性**：遵循 InfiniTrain 的 Dispatcher + Autograd + REGISTER_KERNEL 模式 -4. **类型安全**：支持 float32 和 bfloat16，backward 使用 float32 累积保证精度 - -## 3. 详细设计 - -### 3.1 CUDA Kernel 设计 - -#### 3.1.1 前向 Kernel - -**核心算法**：带有在线 Softmax 的分块注意力计算。 - -``` -输入: Q [B, H_q, N, d], K [B, H_kv, N, d], V [B, H_kv, N, d] -输出: O [B, H_q, N, d], L [B, H_q, N] (logsumexp) - -对每个 (batch, q_head, q_tile) 分配一个 thread block: - 将 Q 的对应 tile 加载到 shared memory: sQ [Br × d] - 初始化: row_m = -inf, row_l = 0, sO = 0 - - FOR 每个 KV tile: - 加载 K tile 到 sKV [Bc × d] - 计算 S = sQ @ sKV^T × scale [Br × Bc] - 应用 causal mask（如启用） - - 在线 softmax 更新: - m_new = max(row_m, rowmax(S)) - P = exp(S - m_new) - rescale = exp(row_m - m_new) - sO = rescale × sO - row_l = rescale × row_l + rowsum(P) - row_m = m_new - - 加载 V tile 到 sKV [Bc × d] - sO += P @ sKV - - 归一化: O = sO / row_l - 写回: L = row_m + log(row_l) -``` - -**Shared Memory 布局**： -| 区域 | 大小 | 用途 | -|------|------|------| -| sQ | Br × d | Query tile (float) | -| sKV | Bc × d | Key/Value tile (复用) | -| sS | Br × Bc | 注意力分数 / 概率 | -| row_m | Br | 行最大值 | -| row_l | Br | 行求和 | -| sO | Br × d | 输出累积器 | - -**总计**: $(2 B_r d + B_c d + B_r B_c + 2 B_r) \times 4$ bytes - -#### 3.1.2 反向 Kernel - -**核心算法**：基于重计算的反向传播，避免存储 $N \times N$ 注意力矩阵。 - -``` -输入: dO, Q, K, V, O, L (logsumexp) -输出: dQ [float], dK [float], dV [float] - -预计算: D[qi] = sum_c dO[qi][c] × O[qi][c] - -对每个 (batch, q_head, q_tile): - 加载 Q, dO tile 到 shared memory - 初始化 dQ accumulator = 0 - - FOR 每个 KV tile: - 加载 K tile - 重计算: S = Q @ K^T × scale - 重计算: P = exp(S - L) (含 causal mask, dropout) - - dV += P^T @ dO (atomicAdd 到 float buffer) - - 加载 V tile - dP = dO @ V^T - dS = P × (dP - D) - - 重新加载 K tile - dQ += dS @ K × scale - dK += dS^T @ Q × scale (atomicAdd 到 float buffer) - - 写回 dQ -``` - -**关键设计决策**： - -1. **Float 梯度缓冲区**：dK、dV 使用 float32 全局缓冲区 + atomicAdd，确保 GQA 场景多个 Q head 映射到同一 KV head 时的正确性，同时避免 bf16 atomicAdd 不可用的问题。 -2. **类型转换 Kernel**：反向完成后，使用 `ConvertFloatToType` kernel 将 float32 梯度转换为目标类型 (如 bf16)。 - -#### 3.1.3 GQA 支持 - -Grouped Query Attention 通过 head 映射实现： -```cpp -kv_head_idx = H_kv == H_q ? head_idx : head_idx / (H_q / H_kv); -``` - -- 前向：多个 Q head 共享同一 KV head，直接读取对应的 K/V -- 反向：多个 Q head 的梯度通过 atomicAdd 累积到同一 KV head 的 dK/dV - -### 3.2 Autograd Function - -`ScaledDotProductAttention` 继承 `autograd::Function`： - -- **Forward**: 校验输入维度，计算 scale，通过 Dispatcher 调用 CUDA kernel -- **SetupContext**: 保存 {Q, K, V, O, L} 共 5 个张量用于反向计算 -- **Backward**: 通过 Dispatcher 调用反向 CUDA kernel，返回 {dQ, dK, dV} - -### 3.3 Functional API - -```cpp -std::shared_ptr ScaledDotProductAttention( - const std::shared_ptr &query, // [B, H_q, N, d] - const std::shared_ptr &key, // [B, H_kv, N, d] - const std::shared_ptr &value, // [B, H_kv, N, d] - bool is_causal = false, - float dropout_p = 0.0f, - std::optional scale = std::nullopt); -``` - -### 3.4 模型集成 - -#### GPT-2 (MHA) - -```cpp -if (config_.flash) { - // Q, K, V 已经是 [B, h, T, d] 布局 - y = nn::function::ScaledDotProductAttention(q, k, v, /*is_causal=*/true); -} else { - // 原始小算子路径: matmul -> mask -> softmax -> matmul -} -``` - -#### LLaMA-3 (GQA) - -```cpp -if (config_.flash) { - // FlashAttention 原生支持 GQA，无需 RepeatKV - q = q->Transpose(1, 2); // [B, H_local, T, D] - k = k->Transpose(1, 2); // [B, KV_local, T, D] - v = v->Transpose(1, 2); - y = nn::function::ScaledDotProductAttention(q, k, v, /*is_causal=*/true); -} else { - k = RepeatKV(k, n_rep_); // 展开 KV heads - v = RepeatKV(v, n_rep_); - // 原始路径... -} -``` - -LLaMA-3 的 FlashAttention 路径跳过了 RepeatKV 操作，既节省了显存（避免复制 KV），又避免了额外的 transpose 开销。 - -## 4. Kernel 参数配置 - -| 参数 | 值 | 说明 | -|------|----|------| -| Br (Query Tile) | 32 | Query 维度分块大小 | -| Bc (KV Tile) | 32 | Key/Value 维度分块大小 | -| NUM_THREADS | 128 | 每个 thread block 的线程数 | -| 支持类型 | float32, bfloat16 | 通过模板特化 | -| 支持 head_dim | 任意 | 运行时参数 | -| CUDA Arch | sm_75, sm_80, sm_90 | A100 主要使用 sm_80 | - -## 5. 性能评估报告 - -### 5.1 实验环境 - -**硬件环境** - -| 项目 | 规格 | -|------|------| -| GPU | NVIDIA A100-SXM4-80GB × 8 | -| GPU 显存 | 80 GB HBM2e | -| CPU | 64 cores | -| 内存 | 512 GB | - -**软件环境** - -| 项目 | 版本 | -|------|------| -| OS | Ubuntu 24.04 LTS | -| CUDA | 12.8 | -| CUDA Driver | 570.86.15 | -| 编译器 | GCC 13 + NVCC 12.8 | -| CMake | 3.31.4 | -| 构建选项 | `-DUSE_CUDA=ON -DUSE_NCCL=ON` | - -### 5.2 实验配置 - -| 参数 | GPT-2 124M | LLaMA-3.2 1B | -|------|-----------|---------------| -| 模型参数量 | 124M | 1.24B | -| n_head / n_kv_head | 12 / 12 (MHA) | 32 / 8 (GQA) | -| head_dim | 64 | 64 | -| batch_size | 4 | 4 | -| sequence_length | 256 | 256 | -| dtype | float32 | float32 | -| 迭代次数 | 20 | 10 | -| overfit_single_batch | false | false | - -### 5.3 GPT-2 性能对比 - -| 指标 | Baseline (小算子拼接) | FlashAttention | 加速比/变化 | -|------|----------------------|----------------|-------------| -| 每步平均耗时 | ~174 ms | ~126 ms | **1.38× (Speedup)** | -| 吞吐率 (tokens/s) | ~5,880 | ~8,100 | **+37.8%** | -| GPU 显存占用 (峰值) | ~3,936 MB (稳定) | ~7,223 MB (初始) | +83.5% | -| Step 20 Loss | 4.063 | 4.097 | ΔLoss < 1% | - -**分析**： -- **速度提升**：FlashAttention 将注意力计算的多次全局内存读写融合为分块 shared memory 操作，减少了 HBM 带宽瓶颈，实现 1.38× 加速。 -- **显存变化**：当前实现在反向传播中分配临时 float32 梯度缓冲区用于 atomicAdd（保证 GQA + bf16 的正确性），导致显存占用高于 baseline。这是计算正确性与内存的权衡，可通过内存池预分配优化。 -- **正确性**：Flash 路径与 baseline 在相同初始权重下 Loss 差异 < 1%，数值精度对齐。 - -### 5.4 LLaMA-3.2 1B 性能对比 - -| 指标 | Baseline (小算子拼接) | FlashAttention | 说明 | -|------|----------------------|----------------|------| -| 训练 Loss 变化 | 4.90 → 4.21 (10步) | 4.90 → 4.21 (10步) | 收敛一致 | -| 吞吐率 (tokens/s) | ~1,100 | ~1,300 | +18% | -| GQA 支持 | RepeatKV 展开 | 原生 kernel 内处理 | 节省 KV 复制开销 | - -**分析**： -- LLaMA-3 使用 GQA (H_q=32, H_kv=8, n_rep=4)，FlashAttention 在 kernel 内通过 head 映射原生处理 GQA，无需调用 RepeatKV 展开 K/V，节省了一次完整的 KV tensor 复制。 -- 反向传播中多个 Q head 的梯度正确地通过 atomicAdd 累积到对应的 KV head。 - -### 5.5 正确性验证 - -| 模型 | 验证方法 | 结果 | -|------|---------|------| -| GPT-2 (MHA) | 相同权重、相同数据，对比 step 20 的 loss | Flash: 4.097 vs Baseline: 4.063, ΔLoss < 1% | -| LLaMA-3 (GQA) | 相同权重、相同数据，对比 loss 曲线 | 收敛趋势一致，4.90 → 4.21 | - -结论：FlashAttention 与原始小算子拼接版本在训练精度上对齐，浮点差异在可接受范围内。 - -## 6. 已知限制与改进方向 - -### 6.1 当前限制 - -1. **Shared Memory 受限**：使用 float32 shared memory 限制了可处理的 head_dim 大小 -2. **反向传播内存**：每次反向调用分配临时 float32 梯度缓冲区 -3. **Tiling 粒度**：Br=Bc=32 固定配置，未针对不同 head_dim 进行自适应调优 - -### 6.2 未来改进 - -1. **Register Tiling**：将部分 shared memory 数据提升到寄存器，提高计算密度 -2. **Warp-level Primitives**：使用 `__shfl_*` 指令加速归约操作 -3. **自适应 Tile Size**：根据 head_dim 和 GPU SM 数量动态选择 Br, Bc -4. **Tensor Core 加速**：利用 WMMA 指令在 A100 的 Tensor Core 上执行矩阵乘法 -5. **内存池**：预分配反向传播的 float32 缓冲区避免重复分配 - -## 7. 使用方式 - -### 7.1 编译 - -```bash -mkdir -p build && cd build -cmake -DUSE_CUDA=ON -DUSE_NCCL=ON .. -make -j$(nproc) -``` - -### 7.2 手动运行 - -```bash -# GPT-2 with FlashAttention -./gpt2 \ - --llmc_filepath= \ - --input_bin= \ - --flash \ - --batch_size=4 --sequence_length=256 - -# LLaMA-3 with FlashAttention (含 GQA) -./llama3 \ - --llmc_filepath= \ - --input_bin= \ - --flash \ - --batch_size=4 --sequence_length=256 -``` - -不传 `--flash` 即走原始小算子路径，行为完全不变。 - -### 7.3 完整运行脚本（端到端验证） - -使用提供的 `test_config_flash.json` 配合已有的 `run_models_and_profile.bash` 一键运行所有对比实验： - -```bash -# 在 scripts/ 目录下执行 -cd scripts -bash run_models_and_profile.bash test_config_flash.json -``` - -该脚本会自动： -1. 编译项目 -2. 依次运行 baseline（无 flash）和 flash 版本的 GPT-2 和 LLaMA-3 实验 -3. 覆盖多种配置：float32 / bfloat16，seq_len = 64 / 256 / 512 -4. 所有日志保存到 `logs_flash/` 目录下 - -`test_config_flash.json` 中定义了如下测试对： - -| 测试 ID | dtype | seq_len | batch | flash | 说明 | -|---------|-------|---------|-------|-------|------| -| baseline_fp32_seq64 | float32 | 64 | 4 | ✗ | 短序列基线 | -| flash_fp32_seq64 | float32 | 64 | 4 | ✓ | 短序列 flash | -| baseline_fp32_seq256 | float32 | 256 | 4 | ✗ | 中等序列基线 | -| flash_fp32_seq256 | float32 | 256 | 4 | ✓ | 中等序列 flash | -| baseline_fp32_seq512 | float32 | 512 | 2 | ✗ | 长序列基线 | -| flash_fp32_seq512 | float32 | 512 | 2 | ✓ | 长序列 flash | -| baseline_bf16_seq256 | bfloat16 | 256 | 4 | ✗ | bf16 基线 | -| flash_bf16_seq256 | bfloat16 | 256 | 4 | ✓ | bf16 flash | - -**注意**：运行前需根据实际环境修改 `test_config_flash.json` 中的数据路径变量： -- `GPT2_INPUT_BIN`、`GPT2_LLMC_FILEPATH` -- `LLAMA3_INPUT_BIN`、`LLAMA3_LLMC_FILEPATH` From 9b2105348dee3641d5fdc36e1f72911aa0ffe3b2 Mon Sep 17 00:00:00 2001 From: LiaoYFBH <2273398935@qq.com> Date: Mon, 9 Mar 2026 21:14:52 +0800 Subject: [PATCH 04/13] update .gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 50b9fa06..5f0810ff 100644 --- a/.gitignore +++ b/.gitignore @@ -5,3 +5,4 @@ build/ *.log *.report.rank* *.records.log.rank* +docs/flash_attention_design.md From bc8cef3096524916453c0121b90f046170ae7533 Mon Sep 17 00:00:00 2001 From: LiaoYFBH <2273398935@qq.com> Date: Sun, 15 Mar 2026 17:40:36 +0800 Subject: [PATCH 05/13] feat: complete flash attention operators and experiment reports --- .gitignore | 2 +- .gitmodules | 6 +-- CMakeLists.txt | 70 +++++++++++++++++++++++++---- scripts/run_models_and_profile.bash | 2 + scripts/test_config_flash.json | 2 +- 5 files changed, 68 insertions(+), 14 deletions(-) diff --git a/.gitignore b/.gitignore index 5f0810ff..f8e4f930 100644 --- a/.gitignore +++ b/.gitignore @@ -5,4 +5,4 @@ build/ *.log *.report.rank* *.records.log.rank* -docs/flash_attention_design.md +*.md diff --git a/.gitmodules b/.gitmodules index 470cf466..04925b70 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,9 +1,9 @@ [submodule "third_party/glog"] path = third_party/glog - url = git@github.com:google/glog.git + url = https://github.com/google/glog.git [submodule "third_party/gflags"] path = third_party/gflags - url = git@github.com:gflags/gflags.git + url = https://github.com/gflags/gflags.git [submodule "third_party/eigen"] path = third_party/eigen - url = git@github.com:InfiniTensor/eigen-mirror.git + url = https://github.com/InfiniTensor/eigen-mirror.git diff --git a/CMakeLists.txt b/CMakeLists.txt index 74536707..47be6bce 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -80,9 +80,17 @@ if(USE_CUDA) # CUDA compilation options set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-extended-lambda --expt-relaxed-constexpr") + # FlashAttention-2 support (optional) + option(USE_FLASH_ATTN "Enable FlashAttention-2 support" OFF) + # Only compile CUDA kernels / cuda sources here (your original used src/*.cu) file(GLOB_RECURSE CUDA_KERNELS ${PROJECT_SOURCE_DIR}/infini_train/src/*.cu) + # When FlashAttention is disabled, exclude flash_attention.cu from framework kernels + if(NOT USE_FLASH_ATTN) + list(FILTER CUDA_KERNELS EXCLUDE REGEX ".*flash_attention\\.cu$") + endif() + add_library(infini_train_cuda_kernels STATIC ${CUDA_KERNELS}) set_target_properties(infini_train_cuda_kernels PROPERTIES CUDA_ARCHITECTURES "75;80;90") @@ -94,6 +102,37 @@ if(USE_CUDA) CUDA::cuda_driver ) + # Build FlashAttention-2 as a separate static library when enabled + if(USE_FLASH_ATTN) + add_compile_definitions(USE_FLASH_ATTN=1) + message(STATUS "FlashAttention-2 support enabled") + + # FlashAttention-2 source files + file(GLOB FLASH_ATTN_SRCS + ${PROJECT_SOURCE_DIR}/third_party/flash-attention/csrc/flash_attn/src/*.cu) + + add_library(flash_attn STATIC ${FLASH_ATTN_SRCS}) + set_target_properties(flash_attn PROPERTIES CUDA_ARCHITECTURES "80;90") + + target_include_directories(flash_attn PUBLIC + ${PROJECT_SOURCE_DIR}/third_party/flash-attention/csrc/flash_attn + ${PROJECT_SOURCE_DIR}/third_party/flash-attention/csrc/flash_attn/src + ${PROJECT_SOURCE_DIR}/third_party/cutlass/include + ) + + target_compile_options(flash_attn PRIVATE + $<$:--expt-relaxed-constexpr --expt-extended-lambda -O2>) + + # Let the framework kernel find flash_attn headers + target_include_directories(infini_train_cuda_kernels PUBLIC + ${PROJECT_SOURCE_DIR}/third_party/flash-attention/csrc/flash_attn + ${PROJECT_SOURCE_DIR}/third_party/flash-attention/csrc/flash_attn/src + ${PROJECT_SOURCE_DIR}/third_party/cutlass/include + ) + + target_link_libraries(infini_train_cuda_kernels PUBLIC flash_attn) + endif() + if(USE_NCCL) message(STATUS "Add USE_NCCL, use NCCL with CUDA") list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake) @@ -139,15 +178,28 @@ endif() # ------------------------------------------------------------------------------ function(link_infini_train_exe target_name) if(USE_CUDA) - target_link_libraries(${target_name} PRIVATE - "-Wl,--start-group" - "-Wl,--whole-archive" - infini_train - infini_train_cpu_kernels - infini_train_cuda_kernels - "-Wl,--no-whole-archive" - "-Wl,--end-group" - ) + if(USE_FLASH_ATTN) + target_link_libraries(${target_name} PRIVATE + "-Wl,--start-group" + "-Wl,--whole-archive" + infini_train + infini_train_cpu_kernels + infini_train_cuda_kernels + flash_attn + "-Wl,--no-whole-archive" + "-Wl,--end-group" + ) + else() + target_link_libraries(${target_name} PRIVATE + "-Wl,--start-group" + "-Wl,--whole-archive" + infini_train + infini_train_cpu_kernels + infini_train_cuda_kernels + "-Wl,--no-whole-archive" + "-Wl,--end-group" + ) + endif() else() target_link_libraries(${target_name} PRIVATE "-Wl,--start-group" diff --git a/scripts/run_models_and_profile.bash b/scripts/run_models_and_profile.bash index 1cf27935..dcad250a 100755 --- a/scripts/run_models_and_profile.bash +++ b/scripts/run_models_and_profile.bash @@ -1,6 +1,8 @@ #!/bin/bash set -e +export TMPDIR=/data/shared/$USER_tmp +mkdir -p $TMPDIR set -o pipefail CONFIG_FILE="${1:-test_config.json}" diff --git a/scripts/test_config_flash.json b/scripts/test_config_flash.json index bf332ebf..4de7b0b3 100644 --- a/scripts/test_config_flash.json +++ b/scripts/test_config_flash.json @@ -13,7 +13,7 @@ { "id": "build_flash", "profile": false, - "cmd": "cmake -DUSE_CUDA=ON -DUSE_NCCL=ON .. && make -j" + "cmd": "cmake -DUSE_CUDA=ON -DUSE_NCCL=ON .. && make -j4" } ], "tests": [ From aaaf3e4f9b5d54622348935467d84ece2aa4232e Mon Sep 17 00:00:00 2001 From: LiaoYFBH <2273398935@qq.com> Date: Sun, 15 Mar 2026 22:35:26 +0800 Subject: [PATCH 06/13] =?UTF-8?q?=E8=A7=A3=E5=86=B3=E6=98=BE=E5=AD=98?= =?UTF-8?q?=E6=B3=84=E9=9C=B2=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../autograd/scaled_dot_product_attention.h | 1 + .../autograd/scaled_dot_product_attention.cc | 24 +++++++++++++++---- .../cuda/scaled_dot_product_attention.cu | 15 +++++------- scripts/test_config.json | 8 +++---- 4 files changed, 30 insertions(+), 18 deletions(-) diff --git a/infini_train/include/autograd/scaled_dot_product_attention.h b/infini_train/include/autograd/scaled_dot_product_attention.h index 5e136e14..5efbe2be 100644 --- a/infini_train/include/autograd/scaled_dot_product_attention.h +++ b/infini_train/include/autograd/scaled_dot_product_attention.h @@ -41,6 +41,7 @@ class ScaledDotProductAttention : public Function { bool is_causal_ = false; float dropout_p_ = 0.0f; std::optional scale_; + std::shared_ptr logsumexp_; }; } // namespace infini_train::autograd diff --git a/infini_train/src/autograd/scaled_dot_product_attention.cc b/infini_train/src/autograd/scaled_dot_product_attention.cc index 4b140dcf..037432e6 100644 --- a/infini_train/src/autograd/scaled_dot_product_attention.cc +++ b/infini_train/src/autograd/scaled_dot_product_attention.cc @@ -49,26 +49,40 @@ ScaledDotProductAttention::Forward(const std::vector> &i auto results = Dispatcher::Instance().Call>>( {device, "FlashAttentionForward"}, query, key, value, scale, is_causal_, dropout_p_); - return results; + logsumexp_ = results[1]; + return {results[0]}; } void ScaledDotProductAttention::SetupContext(const std::vector> &input_tensors, const std::vector> &output_tensors) { // Save inputs and forward outputs needed for backward - // output_tensors[0] = O, output_tensors[1] = L (logsumexp) - saved_tensors_ = {input_tensors[0], input_tensors[1], input_tensors[2], output_tensors[0], output_tensors[1]}; + // output_tensors[0] = O + + // Allocate temporary float buffers here to associate their lifecycle with the graph node + auto dQ_float = std::make_shared(input_tensors[0]->Dims(), DataType::kFLOAT32, input_tensors[0]->GetDevice()); + auto dK_float = std::make_shared(input_tensors[1]->Dims(), DataType::kFLOAT32, input_tensors[1]->GetDevice()); + auto dV_float = std::make_shared(input_tensors[2]->Dims(), DataType::kFLOAT32, input_tensors[2]->GetDevice()); + + saved_tensors_ = {input_tensors[0], input_tensors[1], input_tensors[2], output_tensors[0], logsumexp_, dQ_float, dK_float, dV_float}; + logsumexp_ = nullptr; // Clear temporary reference } std::vector> ScaledDotProductAttention::Backward(const std::vector> &grad_outputs) { CHECK_EQ(grad_outputs.size(), 1) << "Expected 1 gradient output (dO)"; - CHECK_EQ(saved_tensors_.size(), 5) << "Expected 5 saved tensors: Q, K, V, O, L"; + CHECK_EQ(saved_tensors_.size(), 8) << "Expected 8 saved tensors: Q, K, V, O, L, dQ_f, dK_f, dV_f"; const auto &query = saved_tensors_[0]; const auto &key = saved_tensors_[1]; const auto &value = saved_tensors_[2]; const auto &output = saved_tensors_[3]; const auto &logsumexp = saved_tensors_[4]; + + // Pass temporary buffers via dispatcher + const auto &dQ_float = saved_tensors_[5]; + const auto &dK_float = saved_tensors_[6]; + const auto &dV_float = saved_tensors_[7]; + const auto &grad_output = grad_outputs[0]; const auto d = query->Dims()[3]; @@ -79,7 +93,7 @@ ScaledDotProductAttention::Backward(const std::vector> & // Call the fused FlashAttention backward kernel // Returns: {dQ, dK, dV} auto grads = Dispatcher::Instance().Call>>( - {device, "FlashAttentionBackward"}, grad_output, query, key, value, output, logsumexp, scale, is_causal_, + {device, "FlashAttentionBackward"}, grad_output, query, key, value, output, logsumexp, dQ_float, dK_float, dV_float, scale, is_causal_, dropout_p_); return grads; diff --git a/infini_train/src/kernels/cuda/scaled_dot_product_attention.cu b/infini_train/src/kernels/cuda/scaled_dot_product_attention.cu index 0e9ec4ab..3038f243 100644 --- a/infini_train/src/kernels/cuda/scaled_dot_product_attention.cu +++ b/infini_train/src/kernels/cuda/scaled_dot_product_attention.cu @@ -506,6 +506,7 @@ void LaunchFlashAttnBackward(const std::shared_ptr &dO, const std::share const std::shared_ptr &K, const std::shared_ptr &V, const std::shared_ptr &O, const std::shared_ptr &L, std::shared_ptr &dQ, std::shared_ptr &dK, std::shared_ptr &dV, + const std::shared_ptr &dQ_float, const std::shared_ptr &dK_float, const std::shared_ptr &dV_float, float scale, bool is_causal, float dropout_p, cudaStream_t stream) { const auto &dims = Q->Dims(); const int B = dims[0]; @@ -527,11 +528,6 @@ void LaunchFlashAttnBackward(const std::shared_ptr &dO, const std::share unsigned long long rng_seed = 42; - // Allocate float buffers for gradient accumulation (required for atomicAdd with GQA + bf16) - auto dQ_float = std::make_shared(Q->Dims(), DataType::kFLOAT32, Q->GetDevice()); - auto dK_float = std::make_shared(K->Dims(), DataType::kFLOAT32, K->GetDevice()); - auto dV_float = std::make_shared(V->Dims(), DataType::kFLOAT32, V->GetDevice()); - cudaMemsetAsync(dQ_float->DataPtr(), 0, dQ_float->NumElements() * sizeof(float), stream); cudaMemsetAsync(dK_float->DataPtr(), 0, dK_float->NumElements() * sizeof(float), stream); cudaMemsetAsync(dV_float->DataPtr(), 0, dV_float->NumElements() * sizeof(float), stream); @@ -603,8 +599,9 @@ std::vector> FlashAttentionForward(const std::shared_ptr std::vector> FlashAttentionBackward(const std::shared_ptr &grad_output, const std::shared_ptr &query, const std::shared_ptr &key, const std::shared_ptr &value, - const std::shared_ptr &output, const std::shared_ptr &logsumexp, float scale, - bool is_causal, float dropout_p) { + const std::shared_ptr &output, const std::shared_ptr &logsumexp, + const std::shared_ptr &dQ_float, const std::shared_ptr &dK_float, const std::shared_ptr &dV_float, + float scale, bool is_causal, float dropout_p) { auto dtype = query->Dtype(); auto device = query->GetDevice(); @@ -616,10 +613,10 @@ FlashAttentionBackward(const std::shared_ptr &grad_output, const std::sh switch (dtype) { DISPATCH_CASE(WRAP(LaunchFlashAttnBackward(grad_output, query, key, value, output, logsumexp, dQ, dK, dV, - scale, is_causal, dropout_p, stream);), + dQ_float, dK_float, dV_float, scale, is_causal, dropout_p, stream);), DataType::kFLOAT32) DISPATCH_CASE(WRAP(LaunchFlashAttnBackward(grad_output, query, key, value, output, logsumexp, dQ, - dK, dV, scale, is_causal, dropout_p, stream);), + dK, dV, dQ_float, dK_float, dV_float, scale, is_causal, dropout_p, stream);), DataType::kBFLOAT16) default: LOG(FATAL) << "FlashAttention backward: unsupported dtype"; diff --git a/scripts/test_config.json b/scripts/test_config.json index 5659b516..3d66ff7b 100644 --- a/scripts/test_config.json +++ b/scripts/test_config.json @@ -1,10 +1,10 @@ { "variables": { "BUILD_DIR": "../build", - "GPT2_INPUT_BIN": "../../data/llmc/gpt2/tinyshakespeare/tiny_shakespeare_train.bin", - "GPT2_LLMC_FILEPATH": "../../data/llmc/gpt2/gpt2_124M.bin", - "LLAMA3_INPUT_BIN": "../../data/llmc/llama3/tinyshakespeare/tiny_shakespeare_train.bin", - "LLAMA3_LLMC_FILEPATH": "../../data/llmc/llama3/llama3.2_1B_fp32.bin", + "GPT2_INPUT_BIN": "/data/shared/InfiniTrain-dev/data/llmc/gpt2/tinyshakespeare/tiny_shakespeare_train.bin", + "GPT2_LLMC_FILEPATH": "/data/shared/InfiniTrain-dev/data/llmc/gpt2/gpt2_124M.bin", + "LLAMA3_INPUT_BIN": "/data/shared/InfiniTrain-dev/data/llmc/llama3/tinyshakespeare/tiny_shakespeare_train.bin", + "LLAMA3_LLMC_FILEPATH": "/data/shared/InfiniTrain-dev/data/llmc/llama3/llama3.2_1B_fp32.bin", "PROFILE_LOG_DIR": "./profile_logs", "LOG_DIR": "./logs", "COMPARE_LOG_DIR": "" From f5269407b13c23c162ceabe75c52b22710a162aa Mon Sep 17 00:00:00 2001 From: LiaoYFBH <2273398935@qq.com> Date: Mon, 16 Mar 2026 10:35:44 +0800 Subject: [PATCH 07/13] plotted metrics curves and update final evaluation visualizations code --- docs/images/gpt2_loss_curve.png | Bin 0 -> 48632 bytes docs/images/gpt2_memory_curve.png | Bin 0 -> 55065 bytes docs/images/llama3_loss_curve.png | Bin 0 -> 48187 bytes docs/images/llama3_memory_curve.png | Bin 0 -> 58668 bytes plot_metrics.py | 88 ++++++++++++++++++++++++++++ 5 files changed, 88 insertions(+) create mode 100644 docs/images/gpt2_loss_curve.png create mode 100644 docs/images/gpt2_memory_curve.png create mode 100644 docs/images/llama3_loss_curve.png create mode 100644 docs/images/llama3_memory_curve.png create mode 100644 plot_metrics.py diff --git a/docs/images/gpt2_loss_curve.png 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docs/flash_attention_design.md | 414 +++++++++++++++++++++++++++++++++ 1 file changed, 414 insertions(+) create mode 100644 docs/flash_attention_design.md diff --git a/docs/flash_attention_design.md b/docs/flash_attention_design.md new file mode 100644 index 00000000..bc186bd2 --- /dev/null +++ b/docs/flash_attention_design.md @@ -0,0 +1,414 @@ +# FlashAttention 接入设计文档 + +## 1. 概述 + +### 1.1 任务目标 + +在 InfiniTrain 框架中实现 FlashAttention v2 算法的完整接入，包括： + +- 手写 FlashAttention CUDA kernel（前向 + 反向传播） +- 支持 causal mask、可配置 scale、dropout、GQA +- 集成到框架的 Autograd 和 Dispatcher 系统 +- 在 GPT-2 和 LLaMA-3 模型中通过 `--flash` 命令行开关启用 + +### 1.2 算法原理 + +FlashAttention v2 的核心思想是通过 **IO-aware tiling** 将注意力计算分块执行，避免显式构造 $N \times N$ 的注意力矩阵。其关键技术包括： + +1. **分块计算 (Tiling)**：将 Q 分成大小为 $B_r$ 的块，K/V 分成大小为 $B_c$ 的块 +2. **在线 Softmax (Online Softmax)**：使用 running max 和 running sum 避免两遍扫描 +3. **重计算 (Recomputation)**：反向传播时重新计算注意力权重 $P$，避免存储 $O(N^2)$ 中间结果 +4. **数值稳定性**：所有中间计算使用 float32 + +标准注意力的复杂度： +$$\text{memory: } O(N^2), \quad \text{IO: } O(N^2 d)$$ + +FlashAttention 的复杂度： +$$\text{memory: } O(N), \quad \text{IO: } O(N^2 d^2 / M)$$ + +其中 $M$ 是 SRAM（shared memory）大小。 + +### 1.3 参考文献 + +- Dao, T. (2023). FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning. arXiv:2307.08691 + +## 2. 架构设计 + +### 2.1 整体架构 + +``` +用户代码 (GPT-2/LLaMA-3) + │ nn::function::ScaledDotProductAttention(Q, K, V, is_causal=true) + ▼ +nn::functional 层 + │ 创建 autograd::ScaledDotProductAttention Function + │ 调用 Apply({Q, K, V}) + ▼ +Autograd 层 (ScaledDotProductAttention) + │ Forward: Dispatcher -> "FlashAttentionForward" + │ SetupContext: 保存 {Q, K, V, O, L} + │ Backward: Dispatcher -> "FlashAttentionBackward" + ▼ +CUDA Kernel 层 (scaled_dot_product_attention.cu) + │ FlashAttnFwdKernel - 分块在线 softmax + P@V + │ FlashAttnBwdKernel - 重计算 + dQ/dK/dV + ▼ +Dispatcher 注册 + REGISTER_KERNEL(kCUDA, FlashAttentionForward, ...) + REGISTER_KERNEL(kCUDA, FlashAttentionBackward, ...) +``` + +### 2.2 文件结构 + +``` +新增文件： + infini_train/include/autograd/scaled_dot_product_attention.h # Autograd Function 声明 + infini_train/src/autograd/scaled_dot_product_attention.cc # Autograd 实现 + infini_train/src/kernels/cuda/scaled_dot_product_attention.cu # CUDA kernel + +修改文件： + infini_train/include/nn/functional.h # 添加 ScaledDotProductAttention 接口 + infini_train/src/nn/functional.cc # 添加实现 + example/gpt2/main.cc # 添加 --flash flag + example/gpt2/net.h # GPT2Config 添加 flash 字段 + example/gpt2/net.cc # 注意力前向添加 flash 分支 + example/llama3/main.cc # 添加 --flash flag + example/llama3/net.h # FromLLMC 接口变更 + example/llama3/net.cc # 注意力前向添加 flash 分支（含 GQA） +``` + +### 2.3 设计原则 + +1. **最小侵入性**：通过新增文件实现核心功能，对现有代码修改最小化 +2. **API 兼容性**：接口对齐 PyTorch `F.scaled_dot_product_attention` +3. **框架一致性**：遵循 InfiniTrain 的 Dispatcher + Autograd + REGISTER_KERNEL 模式 +4. **类型安全**：支持 float32 和 bfloat16，backward 使用 float32 累积保证精度 + +## 3. 详细设计 + +### 3.1 CUDA Kernel 设计 + +#### 3.1.1 前向 Kernel + +**核心算法**：带有在线 Softmax 的分块注意力计算。 + +``` +输入: Q [B, H_q, N, d], K [B, H_kv, N, d], V [B, H_kv, N, d] +输出: O [B, H_q, N, d], L [B, H_q, N] (logsumexp) + +对每个 (batch, q_head, q_tile) 分配一个 thread block: + 将 Q 的对应 tile 加载到 shared memory: sQ [Br × d] + 初始化: row_m = -inf, row_l = 0, sO = 0 + + FOR 每个 KV tile: + 加载 K tile 到 sKV [Bc × d] + 计算 S = sQ @ sKV^T × scale [Br × Bc] + 应用 causal mask（如启用） + + 在线 softmax 更新: + m_new = max(row_m, rowmax(S)) + P = exp(S - m_new) + rescale = exp(row_m - m_new) + sO = rescale × sO + row_l = rescale × row_l + rowsum(P) + row_m = m_new + + 加载 V tile 到 sKV [Bc × d] + sO += P @ sKV + + 归一化: O = sO / row_l + 写回: L = row_m + log(row_l) +``` + +**Shared Memory 布局**： +| 区域 | 大小 | 用途 | +|------|------|------| +| sQ | Br × d | Query tile (float) | +| sKV | Bc × d | Key/Value tile (复用) | +| sS | Br × Bc | 注意力分数 / 概率 | +| row_m | Br | 行最大值 | +| row_l | Br | 行求和 | +| sO | Br × d | 输出累积器 | + +**总计**: $(2 B_r d + B_c d + B_r B_c + 2 B_r) \times 4$ bytes + +#### 3.1.2 反向 Kernel + +**核心算法**：基于重计算的反向传播，避免存储 $N \times N$ 注意力矩阵。 + +``` +输入: dO, Q, K, V, O, L (logsumexp) +输出: dQ [float], dK [float], dV [float] + +预计算: D[qi] = sum_c dO[qi][c] × O[qi][c] + +对每个 (batch, q_head, q_tile): + 加载 Q, dO tile 到 shared memory + 初始化 dQ accumulator = 0 + + FOR 每个 KV tile: + 加载 K tile + 重计算: S = Q @ K^T × scale + 重计算: P = exp(S - L) (含 causal mask, dropout) + + dV += P^T @ dO (atomicAdd 到 float buffer) + + 加载 V tile + dP = dO @ V^T + dS = P × (dP - D) + + 重新加载 K tile + dQ += dS @ K × scale + dK += dS^T @ Q × scale (atomicAdd 到 float buffer) + + 写回 dQ +``` + +**关键设计决策**： + +1. **Float 梯度缓冲区**：dK、dV 使用 float32 全局缓冲区 + atomicAdd，确保 GQA 场景多个 Q head 映射到同一 KV head 时的正确性，同时避免 bf16 atomicAdd 不可用的问题。 +2. **类型转换 Kernel**：反向完成后，使用 `ConvertFloatToType` kernel 将 float32 梯度转换为目标类型 (如 bf16)。 + +#### 3.1.3 GQA 支持 + +Grouped Query Attention 通过 head 映射实现： +```cpp +kv_head_idx = H_kv == H_q ? head_idx : head_idx / (H_q / H_kv); +``` + +- 前向：多个 Q head 共享同一 KV head，直接读取对应的 K/V +- 反向：多个 Q head 的梯度通过 atomicAdd 累积到同一 KV head 的 dK/dV + +### 3.2 Autograd Function + +`ScaledDotProductAttention` 继承 `autograd::Function`： + +- **Forward**: 校验输入维度，计算 scale，通过 Dispatcher 调用 CUDA kernel +- **SetupContext**: 保存 {Q, K, V, O, L} 共 5 个张量用于反向计算 +- **Backward**: 通过 Dispatcher 调用反向 CUDA kernel，返回 {dQ, dK, dV} + +### 3.3 Functional API + +```cpp +std::shared_ptr ScaledDotProductAttention( + const std::shared_ptr &query, // [B, H_q, N, d] + const std::shared_ptr &key, // [B, H_kv, N, d] + const std::shared_ptr &value, // [B, H_kv, N, d] + bool is_causal = false, + float dropout_p = 0.0f, + std::optional scale = std::nullopt); +``` + +### 3.4 模型集成 + +#### GPT-2 (MHA) + +```cpp +if (config_.flash) { + // Q, K, V 已经是 [B, h, T, d] 布局 + y = nn::function::ScaledDotProductAttention(q, k, v, /*is_causal=*/true); +} else { + // 原始小算子路径: matmul -> mask -> softmax -> matmul +} +``` + +#### LLaMA-3 (GQA) + +```cpp +if (config_.flash) { + // FlashAttention 原生支持 GQA，无需 RepeatKV + q = q->Transpose(1, 2); // [B, H_local, T, D] + k = k->Transpose(1, 2); // [B, KV_local, T, D] + v = v->Transpose(1, 2); + y = nn::function::ScaledDotProductAttention(q, k, v, /*is_causal=*/true); +} else { + k = RepeatKV(k, n_rep_); // 展开 KV heads + v = RepeatKV(v, n_rep_); + // 原始路径... +} +``` + +LLaMA-3 的 FlashAttention 路径跳过了 RepeatKV 操作，既节省了显存（避免复制 KV），又避免了额外的 transpose 开销。 + +## 4. Kernel 参数配置 + +| 参数 | 值 | 说明 | +|------|----|------| +| Br (Query Tile) | 32 | Query 维度分块大小 | +| Bc (KV Tile) | 32 | Key/Value 维度分块大小 | +| NUM_THREADS | 128 | 每个 thread block 的线程数 | +| 支持类型 | float32, bfloat16 | 通过模板特化 | +| 支持 head_dim | 任意 | 运行时参数 | +| CUDA Arch | sm_75, sm_80, sm_90 | A100 主要使用 sm_80 | + +## 5. 性能评估报告 + +### 5.1 实验环境 + +运行成功截图 + +![image-20260315231852684](./assets/image-20260315231852684.png) + +**硬件环境** + +| 项目 | 规格 | +|------|------| +| GPU | NVIDIA A100-SXM4-80GB × 8 | +| GPU 显存 | 80 GB HBM2e | +| CPU | 64 cores | +| 内存 | 512 GB | + +**软件环境** + +| 项目 | 版本 | +|------|------| +| OS | Ubuntu 24.04 LTS | +| CUDA | 12.8 | +| CUDA Driver | 570.86.15 | +| 编译器 | GCC 13 + NVCC 12.8 | +| CMake | 3.31.4 | +| 构建选项 | `-DUSE_CUDA=ON -DUSE_NCCL=ON` | + +### 5.2 实验配置 + +| 参数 | GPT-2 124M | LLaMA-3.2 1B | +|------|-----------|---------------| +| 模型参数量 | 124M | 1.24B | +| n_head / n_kv_head | 12 / 12 (MHA) | 32 / 8 (GQA) | +| head_dim | 64 | 64 | +| batch_size | 4 | 4 | +| sequence_length | 256 | 256 | +| dtype | float32 | float32 | +| 迭代次数 | 20 | 10 | +| overfit_single_batch | false | false | + +### 5.3 GPT-2 性能对比 + +![GPT-2 Loss Curve](./images/gpt2_loss_curve.png) +*(图：GPT-2 Seq 256 下，使用 Float32 训练的 Loss 收敛曲线对比。Flash 路径与 Baseline 完全一致)* + +![GPT-2 Memory Curve](./images/gpt2_memory_curve.png) +*(图：GPT-2 不同序列长度下的显存占用对比，FlashAttention 完美表现出恒定显存与较低开销)* + +| 指标 | Baseline (小算子拼接) | FlashAttention | 加速比/变化 | +|------|----------------------|----------------|-------------| +| 每步平均耗时 | 76.5 ms | 126.4 ms | 0.6× | +| 吞吐率 (tokens/s) | 13,493 | 8,097 | 降低 40.0% | +| GPU 显存占用 (峰值) | 3,893 MB | 3,770 MB | **-3.2% (降低)** | +| Step 20 Loss | 4.062876 | 4.062879 | ΔLoss < 0.0001% | + +**分析**： +- **速度变化**：由于未利用 WMMA 等 Tensor Core 指令以及其他深度优化机制，手写的基础 FlashAttention kernel 算力吞吐不敌框架默认的极致优化矩阵乘法路径，吞吐率降低 40.0%（从 13,493 tok/s 降至 8,097 tok/s）。 +- **显存优化**：核心收益体现在显存占用上。得益于重计算策略，Flash 路径显存开销稳态降至 3,770 MB（相比 Baseline 的 3,893 MB 节省 123 MB，降低 3.2%），正确实现了 $O(N^2)$ 中间矩阵缓存的豁免。 +- **正确性**：Flash 路径与 Baseline 结果在 FP32 模式下高度对齐，Step 20 损失差距仅为 0.000003（4.062879 vs 4.062876），相对误差 < 0.0001%。 + +### 5.4 LLaMA-3.2 1B 性能对比 + +![LLaMA-3 Loss Curve](./images/llama3_loss_curve.png) +*(图：LLaMA-3 Seq 256 训练 Loss 收敛曲线对比，由于算法严格对齐，曲线几乎完全重合)* + +![LLaMA-3 Memory Curve](./images/llama3_memory_curve.png) +*(图：LLaMA-3 显存占用随序列长度增加的变化情况。可以看到 Baseline 呈抛物线增长，而 FlashAttention 稳如泰山)* + +| 指标 | Baseline (小算子拼接) | FlashAttention | 说明 | +|------|----------------------|----------------|------| +| 训练 Loss 变化 | 4.37 → 3.53 (10步) → 3.34 (20步) | 4.37 → 3.53 (10步) → 3.34 (20步) | 收敛完全一致 | +| 吞吐率 (tokens/s, Seq 256) | 1,817 | 1,504 | 降低 17.2% | +| 吞吐率 (tokens/s, Seq 512) | 1,767 | 1,261 | 降低 28.6% | +| 显存占用 (Seq 256) | 30,023 MB | 29,447 MB | **节约 576 MB (-1.9%)** | +| 显存占用 (Seq 512) | 30,536 MB | 29,447 MB | **节约 1,089 MB (-3.6%)** | +| GQA 支持 | RepeatKV 展开 | 原生 kernel 内处理 | 节省 KV 复制开销 | + +**分析**： +- 随着 LLaMA-3 序列长度提升至 512，基线的显存以 $O(N^2)$ 继续膨胀（从 30,023 MB 涨至 30,536 MB，增加 513 MB），而 FlashAttention 稳如泰山（依然保持在 29,447 MB），完美验证了算法对超长序列场景的理论显存控制能力。 +- 在 Seq 512 场景下，FlashAttention 相比 Baseline 节省了 1,089 MB 显存（3.6%），显存优势随序列长度增加而更加明显。 +- FlashAttention 原生支持跨头 GQA 特性，免去了原本需要的巨大冗余张量创建逻辑（RepeatKV），验证了其架构实用性。 +- 吞吐率方面，Seq 256 下降低 17.2%，Seq 512 下降低 28.6%，这是由于手写 kernel 未使用 Tensor Core 等深度优化，但显存节省效果显著。 + +### 5.5 正确性验证 + +| 模型 | 验证方法 | 结果 | +|------|---------|------| +| GPT-2 (MHA) | 相同权重、相同数据，对比 step 20 的 loss | Flash: 4.062879 vs Baseline: 4.062876，差异 0.000003 (< 0.0001%) | +| LLaMA-3 (GQA) | 相同权重、相同数据，对比 loss 曲线 | 严格贴合，Step 20: Flash 3.338568 vs Baseline 3.338569，差异 0.000001 | + +结论：FlashAttention 与原始小算子拼接版本在训练精度上对齐，浮点差异在可接受范围内。 + +## 6. 已知限制与改进方向 + +### 6.1 当前限制 + +1. **Shared Memory 受限**：使用 float32 shared memory 限制了可处理的 head_dim 大小 +2. **反向传播内存**：每次反向调用分配临时 float32 梯度缓冲区 +3. **Tiling 粒度**：Br=Bc=32 固定配置，未针对不同 head_dim 进行自适应调优 + +### 6.2 未来改进 + +1. **Register Tiling**：将部分 shared memory 数据提升到寄存器，提高计算密度 +2. **Warp-level Primitives**：使用 `__shfl_*` 指令加速归约操作 +3. **自适应 Tile Size**：根据 head_dim 和 GPU SM 数量动态选择 Br, Bc +4. **Tensor Core 加速**：利用 WMMA 指令在 A100 的 Tensor Core 上执行矩阵乘法 +5. **内存池**：预分配反向传播的 float32 缓冲区避免重复分配 + +## 7. 使用方式 + +### 7.1 编译 + +```bash +mkdir -p build && cd build +cmake -DUSE_CUDA=ON -DUSE_NCCL=ON .. +make -j$(nproc) +``` + +### 7.2 手动运行 + +```bash +# GPT-2 with FlashAttention +./gpt2 \ + --llmc_filepath= \ + --input_bin= \ + --flash \ + --batch_size=4 --sequence_length=256 + +# LLaMA-3 with FlashAttention (含 GQA) +./llama3 \ + --llmc_filepath= \ + --input_bin= \ + --flash \ + --batch_size=4 --sequence_length=256 +``` + +不传 `--flash` 即走原始小算子路径，行为完全不变。 + +### 7.3 完整运行脚本（端到端验证） + +使用提供的 `test_config_flash.json` 配合已有的 `run_models_and_profile.bash` 一键运行所有对比实验： + +```bash +# 在 scripts/ 目录下执行 +cd scripts +bash run_models_and_profile.bash test_config_flash.json +``` + +该脚本会自动： +1. 编译项目 +2. 依次运行 baseline（无 flash）和 flash 版本的 GPT-2 和 LLaMA-3 实验 +3. 覆盖多种配置：float32 / bfloat16，seq_len = 64 / 256 / 512 +4. 所有日志保存到 `logs_flash/` 目录下 + +`test_config_flash.json` 中定义了如下测试对： + +| 测试 ID | dtype | seq_len | batch | flash | 说明 | +|---------|-------|---------|-------|-------|------| +| baseline_fp32_seq64 | float32 | 64 | 4 | ✗ | 短序列基线 | +| flash_fp32_seq64 | float32 | 64 | 4 | ✓ | 短序列 flash | +| baseline_fp32_seq256 | float32 | 256 | 4 | ✗ | 中等序列基线 | +| flash_fp32_seq256 | float32 | 256 | 4 | ✓ | 中等序列 flash | +| baseline_fp32_seq512 | float32 | 512 | 2 | ✗ | 长序列基线 | +| flash_fp32_seq512 | float32 | 512 | 2 | ✓ | 长序列 flash | +| baseline_bf16_seq256 | bfloat16 | 256 | 4 | ✗ | bf16 基线 | +| flash_bf16_seq256 | bfloat16 | 256 | 4 | ✓ | bf16 flash | + +**注意**：运行前需根据实际环境修改 `test_config_flash.json` 中的数据路径变量： +- `GPT2_INPUT_BIN`、`GPT2_LLMC_FILEPATH` +- `LLAMA3_INPUT_BIN`、`LLAMA3_LLMC_FILEPATH` From fc0db030be3ab4c1bcdda698ad45a5f6e4292eb4 Mon Sep 17 00:00:00 2001 From: LiaoYFBH <2273398935@qq.com> Date: Mon, 16 Mar 2026 22:58:59 +0800 Subject: [PATCH 09/13] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E6=8A=A5=E5=91=8A?= =?UTF-8?q?=E5=9B=BE=E7=89=87?= MIME-Version: 1.0 Content-Type: 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100644 scripts/logs_flash/llama3_flash_fp32_seq64.log create mode 100644 scripts/logs_flash/llama3_flash_fp32_seq64.log:Zone.Identifier diff --git a/scripts/logs_flash/build_flash.log b/scripts/logs_flash/build_flash.log new file mode 100644 index 00000000..62044760 --- /dev/null +++ b/scripts/logs_flash/build_flash.log @@ -0,0 +1,345 @@ +[LAST_CMAKE] cmake -DUSE_CUDA=ON -DUSE_NCCL=ON .. && make -j4 +[COMMAND] cmake -DUSE_CUDA=ON -DUSE_NCCL=ON .. && make -j4 +-- The CXX compiler identification is GNU 13.3.0 +-- Detecting CXX compiler ABI info +-- Detecting CXX compiler ABI info - done +-- Check for working CXX compiler: /usr/bin/c++ - skipped +-- Detecting CXX compile features +-- Detecting CXX compile features - done +CMake Deprecation Warning at third_party/gflags/CMakeLists.txt:73 (cmake_minimum_required): + Compatibility with CMake < 3.10 will be removed from a future version of + CMake. + + Update the VERSION argument value. Or, use the ... syntax + to tell CMake that the project requires at least but has been updated + to work with policies introduced by or earlier. + + +-- Looking for C++ include unistd.h +-- Looking for C++ include unistd.h - found +-- Looking for C++ include stdint.h +-- Looking for C++ include stdint.h - found +-- Looking for C++ include inttypes.h +-- Looking for C++ include inttypes.h - found +-- Looking for C++ include sys/types.h +-- Looking for C++ include sys/types.h - found +-- Looking for C++ include sys/stat.h +-- Looking for C++ include sys/stat.h - found +-- Looking for C++ include fnmatch.h +-- Looking for C++ include fnmatch.h - found +-- Looking for C++ include stddef.h +-- Looking for C++ include stddef.h - found +-- Check size of uint32_t +-- Check size of uint32_t - done +-- Looking for strtoll +-- Looking for strtoll - found +-- Performing Test CMAKE_HAVE_LIBC_PTHREAD +-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success +-- Found Threads: TRUE +-- Found Unwind: /usr/include/x86_64-linux-gnu (found version "1.6.2") +-- Looking for _Unwind_Backtrace +-- Looking for _Unwind_Backtrace - found +-- Looking for _Unwind_GetIP +-- Looking for _Unwind_GetIP - found +-- Looking for unw_get_reg +-- Looking for unw_get_reg - found +-- Looking for unw_getcontext +-- Looking for unw_getcontext - found +-- Looking for unw_init_local +-- Looking for unw_init_local - found +-- Looking for unw_step +-- Looking for unw_step - found +-- Looking for C++ include dlfcn.h +-- Looking for C++ include dlfcn.h - found +-- Looking for C++ include elf.h +-- Looking for C++ include elf.h - found +-- Looking for C++ include glob.h +-- Looking for C++ include glob.h - found +-- Looking for C++ include link.h +-- Looking for C++ include link.h - found +-- Looking for C++ include pwd.h +-- Looking for C++ include pwd.h - found +-- Looking for C++ include sys/exec_elf.h +-- Looking for C++ include sys/exec_elf.h - not found +-- Looking for C++ include sys/syscall.h +-- Looking for C++ include sys/syscall.h - found +-- Looking for C++ include sys/time.h +-- Looking for C++ include sys/time.h - found +-- Looking for C++ include sys/utsname.h +-- Looking for C++ include sys/utsname.h - found +-- Looking for C++ include sys/wait.h +-- Looking for C++ include sys/wait.h - found +-- Looking for C++ include syscall.h +-- Looking for C++ include syscall.h - found +-- Looking for C++ include syslog.h +-- Looking for C++ include syslog.h - found +-- Looking for C++ include ucontext.h +-- Looking for C++ include ucontext.h - found +-- Check size of mode_t +-- Check size of mode_t - done +-- Check size of ssize_t +-- Check size of ssize_t - done +-- Looking for dladdr +-- Looking for dladdr - found +-- Looking for fcntl +-- Looking for fcntl - found +-- Looking for posix_fadvise +-- Looking for posix_fadvise - found +-- Looking for pread +-- Looking for pread - found +-- Looking for pwrite +-- Looking for pwrite - found +-- Looking for sigaction +-- Looking for sigaction - found +-- Looking for sigaltstack +-- Looking for sigaltstack - found +-- Looking for backtrace +-- Looking for backtrace - found +-- Looking for backtrace_symbols +-- Looking for backtrace_symbols - found +-- Looking for _chsize_s +-- Looking for _chsize_s - not found +-- Looking for UnDecorateSymbolName +-- Looking for UnDecorateSymbolName - not found +-- Looking for abi::__cxa_demangle +-- Looking for abi::__cxa_demangle - found +-- Looking for __argv +-- Looking for __argv - not found +-- Looking for getprogname +-- Looking for getprogname - not found +-- Looking for program_invocation_short_name +-- Looking for program_invocation_short_name - found +-- Performing Test HAVE___PROGNAME +-- Performing Test HAVE___PROGNAME - Success +-- Performing Test HAVE_PC_FROM_UCONTEXT_uc_mcontext_gregs_REG_PC +-- Performing Test HAVE_PC_FROM_UCONTEXT_uc_mcontext_gregs_REG_PC - Failed +-- Performing Test HAVE_PC_FROM_UCONTEXT_uc_mcontext_gregs_REG_EIP +-- Performing Test HAVE_PC_FROM_UCONTEXT_uc_mcontext_gregs_REG_EIP - Failed +-- Performing Test HAVE_PC_FROM_UCONTEXT_uc_mcontext_gregs_REG_RIP +-- Performing Test HAVE_PC_FROM_UCONTEXT_uc_mcontext_gregs_REG_RIP - Success +-- Looking for gmtime_r +-- Looking for gmtime_r - found +-- Looking for localtime_r +-- Looking for localtime_r - found +-- Performing Test COMPILER_HAS_HIDDEN_VISIBILITY +-- Performing Test COMPILER_HAS_HIDDEN_VISIBILITY - Success +-- Performing Test COMPILER_HAS_HIDDEN_INLINE_VISIBILITY +-- Performing Test COMPILER_HAS_HIDDEN_INLINE_VISIBILITY - Success +-- Performing Test COMPILER_HAS_DEPRECATED_ATTR +-- Performing Test COMPILER_HAS_DEPRECATED_ATTR - Success +-- Found OpenMP_CXX: -fopenmp (found version "4.5") +-- Found OpenMP: TRUE (found version "4.5") +-- The C compiler identification is GNU 13.3.0 +-- Detecting C compiler ABI info +-- Detecting C compiler ABI info - done +-- Check for working C compiler: /usr/bin/cc - skipped +-- Detecting C compile features +-- Detecting C 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a/scripts/logs_flash/build_flash.log:Zone.Identifier b/scripts/logs_flash/build_flash.log:Zone.Identifier new file mode 100644 index 0000000000000000000000000000000000000000..d6c1ec682968c796b9f5e9e080cc6f674b57c766 GIT binary patch literal 25 dcma!!%Fjy;DN4*MPD?F{<>dl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2x Date: Mon, 16 Mar 2026 23:25:36 +0800 Subject: [PATCH 12/13] Remove Zone.Identifier files from logs_flash Co-Authored-By: Claude Opus 4.6 --- scripts/logs_flash/build_flash.log:Zone.Identifier | Bin 25 -> 0 bytes .../gpt2_baseline_bf16_seq256.log:Zone.Identifier | Bin 25 -> 0 bytes .../gpt2_baseline_fp32_seq256.log:Zone.Identifier | Bin 25 -> 0 bytes .../gpt2_baseline_fp32_seq512.log:Zone.Identifier | Bin 25 -> 0 bytes .../gpt2_baseline_fp32_seq64.log:Zone.Identifier | Bin 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scripts/logs_flash/llama3_flash_bf16_seq256.log:Zone.Identifier delete mode 100644 scripts/logs_flash/llama3_flash_fp32_seq256.log:Zone.Identifier delete mode 100644 scripts/logs_flash/llama3_flash_fp32_seq512.log:Zone.Identifier delete mode 100644 scripts/logs_flash/llama3_flash_fp32_seq64.log:Zone.Identifier diff --git a/scripts/logs_flash/build_flash.log:Zone.Identifier b/scripts/logs_flash/build_flash.log:Zone.Identifier deleted file mode 100644 index d6c1ec682968c796b9f5e9e080cc6f674b57c766..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 25 dcma!!%Fjy;DN4*MPD?F{<>dl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2xdl#JyUFr831@K2x Date: Mon, 16 Mar 2026 23:27:24 +0800 Subject: [PATCH 13/13] add lopg_path 2 doc --- docs/flash_attention_design.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/flash_attention_design.md b/docs/flash_attention_design.md index 3f52e072..e3a3bd8b 100644 --- a/docs/flash_attention_design.md +++ b/docs/flash_attention_design.md @@ -248,7 +248,7 @@ LLaMA-3 的 FlashAttention 路径跳过了 RepeatKV 操作，既节省了显存 运行成功截图 ![image-20260315231852684](./assets/image-20260315231852684.png) - +日志文件路径：./scripts/logs_flash **硬件环境** | 项目 | 规格 |