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Merge remote-tracking branch 'origin/cudnn_try2' into cudnn_try2

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ademeure 2024-05-01 15:27:07 +01:00
commit d508463ad7
2 changed files with 336 additions and 17 deletions
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8
test_gpt2.cu
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@ -107,6 +107,12 @@ int main(int argc, char *argv[]) {
cublasCheck(cublasSetMathMode(cublas_handle, cublas_math_mode));
cudaCheck(cudaMalloc(&cublaslt_workspace, cublaslt_workspace_size));
#ifdef ENABLE_CUDNN
checkCudnnErr(cudnnCreate(&cudnn_handle));
cudaCheck(cudaMalloc(&cudnn_workspace, cudnn_workspace_size));
printf("INIT CUDNN %d\n", cudnn_workspace_size);
#endif
// build the GPT-2 model from a checkpoint
GPT2 model;
gpt2_build_from_checkpoint(&model, "gpt2_124M_bf16.bin");
@ -172,7 +178,7 @@ int main(int argc, char *argv[]) {
float logit_accuracy_threshold = 1e-2f;
float loss_diff_threshold = 0.05f;
#if defined(ENABLE_BF16) || defined(ENABLE_F16)
logit_accuracy_threshold = 15.0f;
logit_accuracy_threshold = 25.0f; // 15.0f was too low even without cuDNN?! :(
#endif
// compare the output logits from the forward pass

345
train_gpt2.cu
View file

@ -25,6 +25,7 @@ Also we're using TinyStories here for example as it is a bigger dataset
Example launch using bfloat16 on 4 GPUs, same as above:
mpirun -np 4 ./train_gpt2cu -b 8 -v 200 -s 200 -i data/TinyStories
*/
#define ENABLE_CUDNN // can be enabled via nvcc "-DENABLE_CUDNN"
#include <stdio.h>
#include <stdlib.h>
@ -103,6 +104,23 @@ const ncclDataType_t ncclFloatX = ncclBfloat16;
#endif
#endif
#ifdef ENABLE_CUDNN
#include <cudnn_frontend.h>
namespace fe = cudnn_frontend;
#if CUBLAS_LOWP == CUDA_R_16BF
#define CUDNN_16BIT fe::DataType_t::BFLOAT16
#else
#define CUDNN_16BIT fe::DataType_t::HALF
#endif
static cudnnHandle_t cudnn_handle;
static size_t cudnn_workspace_size = 0; // dynamically allocated as needed (up to 256MiB!)
static void* cudnn_workspace = NULL;
#define checkCudaErr(err) assert((int)err == 0);
#define checkCudnnErr(err) assert((int)err == 0);
#endif // ENABLE_CUDNN
// ----------------------------------------------------------------------------
// CUDA utils
@ -429,6 +447,261 @@ void printf0(const char *format, ...) {
}
}
// ----------------------------------------------------------------------------
// cuDNN path
#ifdef ENABLE_CUDNN
using graph_tensors_fwd = std::tuple<std::shared_ptr<fe::graph::Graph>,
std::shared_ptr<fe::graph::Tensor_attributes>, // Q,
std::shared_ptr<fe::graph::Tensor_attributes>, // K,
std::shared_ptr<fe::graph::Tensor_attributes>, // V,
std::shared_ptr<fe::graph::Tensor_attributes>, // Attn_scale,
std::shared_ptr<fe::graph::Tensor_attributes>, // O
std::shared_ptr<fe::graph::Tensor_attributes>>; // Stats
using graph_tensors_bwd = std::tuple<std::shared_ptr<fe::graph::Graph>,
std::shared_ptr<fe::graph::Tensor_attributes>, // Q,
std::shared_ptr<fe::graph::Tensor_attributes>, // K,
std::shared_ptr<fe::graph::Tensor_attributes>, // V,
std::shared_ptr<fe::graph::Tensor_attributes>, // O
std::shared_ptr<fe::graph::Tensor_attributes>, // dO
std::shared_ptr<fe::graph::Tensor_attributes>, // Stats
std::shared_ptr<fe::graph::Tensor_attributes>, // Attn_scale,
std::shared_ptr<fe::graph::Tensor_attributes>, // dQ,
std::shared_ptr<fe::graph::Tensor_attributes>, // dK,
std::shared_ptr<fe::graph::Tensor_attributes>>; // dV
// Need a cache because graph->build_operation_graph() is slow but everything else seems fast
using cache_type_fwd = std::unordered_map<std::size_t, graph_tensors_fwd>;
using cache_type_bwd = std::unordered_map<std::size_t, graph_tensors_bwd>;
// Loosely based on cuDNN frontend samples functions and massively simplified
template <typename... Args>
auto lookup_cache_or_build_graph_fwd(Args... args) {
static cache_type_fwd user_maintained_cache_fwd;
auto [B, H, T, HS, is_inference_only] = std::make_tuple(args...);
auto graph = std::make_shared<fe::graph::Graph>();
graph->set_io_data_type(CUDNN_16BIT)
.set_intermediate_data_type(fe::DataType_t::FLOAT)
.set_compute_data_type(fe::DataType_t::FLOAT);
// QKV is (B, T, 3, NH, HS) which cuDNN can handle directly without an external permute
auto Q = graph->tensor(fe::graph::Tensor_attributes()
.set_name("Q")
.set_dim({B, H, T, HS})
.set_stride({3 * H * HS * T, HS, 3 * H * HS, 1}));
auto K = graph->tensor(fe::graph::Tensor_attributes()
.set_name("K")
.set_dim({B, H, T, HS})
.set_stride({3 * H * HS * T, HS, 3 * H * HS, 1}));
auto V = graph->tensor(fe::graph::Tensor_attributes()
.set_name("V")
.set_dim({B, H, T, HS})
.set_stride({3 * H * HS * T, HS, 3 * H * HS, 1}));
auto attn_scale = graph->tensor(fe::graph::Tensor_attributes()
.set_name("attn_scale")
.set_dim({1, 1, 1, 1})
.set_stride({1, 1, 1, 1})
.set_is_pass_by_value(true)
.set_data_type(fe::DataType_t::FLOAT));
auto sdpa_options = fe::graph::SDPA_attributes().set_name("flash_attention");
sdpa_options.set_is_inference(is_inference_only);
sdpa_options.set_attn_scale(attn_scale);
sdpa_options.set_causal_mask(true);
// Create the graph operation and get the output tensors back
auto [O, stats] = graph->sdpa(Q, K, V, sdpa_options);
// Output is (B, T, NH, HS) BF16/FP16 and stats for backward pass is (B, NH, T) FP32
O->set_output(true).set_dim({B, H, T, HS}).set_stride({H * HS * T, HS, H * HS, 1});
assert(stats == nullptr || is_inference_only == false);
if (is_inference_only == false) {
stats->set_output(true).set_data_type(fe::DataType_t::FLOAT)
.set_dim({B, H, T, 1})
.set_stride({H * T, T, 1, 1});
}
assert(graph->validate().is_good());
auto key = graph->key();
auto it = user_maintained_cache_fwd.find(key);
if (it != user_maintained_cache_fwd.end()) {
return it->second;
}
// Build the operation graph and execution part (this is the VERY SLOW PART)
assert(graph->build_operation_graph(cudnn_handle).is_good());
auto plans = graph->create_execution_plans({fe::HeurMode_t::A});
assert(graph->check_support(cudnn_handle).is_good());
assert(graph->build_plans(cudnn_handle).is_good());
auto tuple = std::make_tuple(graph, Q, K, V, attn_scale, O, stats);
user_maintained_cache_fwd.insert({key, tuple});
return tuple;
}
template <typename... Args>
auto lookup_cache_or_build_graph_bwd(Args... args) {
static cache_type_bwd user_maintained_cache_bwd;
auto [B, NH, T, HS] = std::make_tuple(args...);
auto graph = std::make_shared<fe::graph::Graph>();
graph->set_io_data_type(CUDNN_16BIT)
.set_intermediate_data_type(fe::DataType_t::FLOAT)
.set_compute_data_type(fe::DataType_t::FLOAT);
// (B, N, 3, NH, HS)
// must come from inp (which means we also need to convert THAT to FP16)
auto Q = graph->tensor(fe::graph::Tensor_attributes()
.set_name("Q")
.set_dim({B, NH, T, HS})
.set_stride({3 * NH * HS * T, HS, 3 * NH * HS, 1}));
auto K = graph->tensor(fe::graph::Tensor_attributes()
.set_name("K")
.set_dim({B, NH, T, HS})
.set_stride({3 * NH * HS * T, HS, 3 * NH * HS, 1}));
auto V = graph->tensor(fe::graph::Tensor_attributes()
.set_name("V")
.set_dim({B, NH, T, HS})
.set_stride({3 * NH * HS * T, HS, 3 * NH * HS, 1}));
auto O = graph->tensor(fe::graph::Tensor_attributes()
.set_name("O")
.set_dim({B, NH, T, HS})
.set_stride({NH * HS * T, HS, NH * HS, 1}));
auto dO = graph->tensor(fe::graph::Tensor_attributes()
.set_name("dO")
.set_dim({B, NH, T, HS})
.set_stride({NH * HS * T, HS, NH * HS, 1}));
auto stats = graph->tensor(fe::graph::Tensor_attributes()
.set_name("stats")
.set_dim({B, NH, T, 1})
.set_stride({NH * T, T, 1, 1})
.set_data_type(fe::DataType_t::FLOAT));
auto attn_scale = graph->tensor(fe::graph::Tensor_attributes()
.set_name("attn_scale")
.set_dim({1, 1, 1, 1})
.set_stride({1, 1, 1, 1})
.set_is_pass_by_value(true)
.set_data_type(fe::DataType_t::FLOAT));
auto sdpa_backward_options = fe::graph::SDPA_backward_attributes()
.set_name("flash_attention_backward")
.set_causal_mask(true)
.set_attn_scale(attn_scale);
// Create the graph operation and get the output tensors back
auto [dQ, dK, dV] = graph->sdpa_backward(Q, K, V, O, dO, stats, sdpa_backward_options);
dQ->set_output(true).set_dim({B, NH, T, HS}).set_stride({3 * NH * HS * T, HS, 3 * NH * HS, 1});
dK->set_output(true).set_dim({B, NH, T, HS}).set_stride({3 * NH * HS * T, HS, 3 * NH * HS, 1});
dV->set_output(true).set_dim({B, NH, T, HS}).set_stride({3 * NH * HS * T, HS, 3 * NH * HS, 1});
assert(graph->validate().is_good());
auto key = graph->key();
auto it = user_maintained_cache_bwd.find(key);
if (it != user_maintained_cache_bwd.end()) {
return it->second;
}
// Build the operation graph and execution part (this is the VERY SLOW PART)
assert(graph->build_operation_graph(cudnn_handle).is_good());
auto plans = graph->create_execution_plans({fe::HeurMode_t::A});
assert(graph->check_support(cudnn_handle).is_good());
assert(graph->build_plans(cudnn_handle).is_good());
auto tuple = std::make_tuple(graph, Q, K, V, O, dO, stats, attn_scale, dQ, dK, dV);
user_maintained_cache_bwd.insert({key, tuple});
return tuple;
}
void attention_forward_cudnn(floatX* out, // output: (B, T, NH, HS)
float* stats, // output for backward pass: (B, NH, T)
floatX* inp, // input: (B, T, 3, NH, HS) QKV
int B, int T, int NH, int C) {
int HS = C / NH; // number of features per head
bool is_inference_only = (stats == nullptr);
// Get graph and tensors from cache (or generate it on first use)
auto [graph, Q, K, V, attn_scale, O, softmax_stats] =
lookup_cache_or_build_graph_fwd(B, NH, T, HS, is_inference_only);
// Prepare all the tensor pointers for executing the graph
void* devPtrQ = inp;
void* devPtrK = (inp + C);
void* devPtrV = (inp + 2 * C);
float attn_scale_cpu = 1.0 / sqrtf(HS);
void* devPtrO = out;
// Build variant pack
std::unordered_map<std::shared_ptr<fe::graph::Tensor_attributes>, void*> variant_pack = {
{Q, devPtrQ}, {K, devPtrK}, {V, devPtrV}, {attn_scale, &attn_scale_cpu}, {O, devPtrO}};
// Add the stats tensor unless we are only doing inference (only needed for backward pass)
if (is_inference_only == false) {
variant_pack[softmax_stats] = stats;
}
// Reallocate the workspace if the required size is greater than the current workspace
// By default, cuDNN uses up to 256MiB of workspace, so we don't want to just allocate the maximum
if (graph->get_workspace_size() > cudnn_workspace_size) {
if (cudnn_workspace_size > 0) {
cudaCheck(cudaFree(cudnn_workspace));
}
cudnn_workspace_size = graph->get_workspace_size();
cudaCheck(cudaMalloc(&cudnn_workspace, cudnn_workspace_size));
}
// Execute graph
assert(graph->execute(cudnn_handle, variant_pack, cudnn_workspace).is_good());
cudaCheck(cudaGetLastError());
}
void attention_backward_cudnn(floatX* dqkvr, // output
floatX* dout, floatX* qkvr, floatX* o, float* stats, // inputs
int B, int T, int NH, int C) {
int HS = C / NH; // number of features per head
// Get graph and tensors from cache (or generate it on first use)
auto [graph, Q, K, V, O, dO, Stats, attn_scale, dQ, dK, dV] =
lookup_cache_or_build_graph_bwd(B, NH, T, HS);
// Prepare all the tensor pointers for executing the graph
void* devPtrQ = qkvr;
void* devPtrK = (qkvr + NH * HS);
void* devPtrV = (qkvr + 2 * NH * HS);
void* devPtrO = o;
void* devPtrdO = dout;
void* devPtrStats = stats;
float attn_scale_cpu = 1.0 / sqrtf(HS);
void* devPtrdQ = dqkvr;
void* devPtrdK = (dqkvr + NH * HS);
void* devPtrdV = (dqkvr + 2 * NH * HS);
// Build variant pack that links each tensor to its data pointer
std::unordered_map<std::shared_ptr<fe::graph::Tensor_attributes>, void*> variant_pack = {
{Q, devPtrQ}, {K, devPtrK}, {V, devPtrV}, {O, devPtrO}, {dO, devPtrdO}, {Stats, devPtrStats},
{dQ, devPtrdQ}, {dK, devPtrdK}, {dV, devPtrdV},
{attn_scale, &attn_scale_cpu}};
// Reallocate the workspace if the required size is greater than the current workspace
// By default, cuDNN uses up to 256MiB of workspace, so we don't want to just allocate the maximum
if (graph->get_workspace_size() > cudnn_workspace_size) {
if (cudnn_workspace_size > 0) {
cudaCheck(cudaFree(cudnn_workspace));
}
cudnn_workspace_size = graph->get_workspace_size();
cudaCheck(cudaMalloc(&cudnn_workspace, cudnn_workspace_size));
}
// Execute graph
assert(graph->execute(cudnn_handle, variant_pack, cudnn_workspace).is_good());
cudaCheck(cudaGetLastError());
}
#endif // ENABLE_CUDNN
// ----------------------------------------------------------------------------
// all the kernels
@ -1460,19 +1733,29 @@ void fill_in_activation_sizes(size_t* act_sizes, size_t B, size_t T, GPT2Config
// Backward pass is conceptually quite different from forward, because we can discard
// the activations of a layer as soon as we're done with it. This lets us aggressively
// reuse memory, so that we need far fewer tensors for backward state.
#ifdef ENABLE_CUDNN
#define NUM_BACKWARD_TENSORS 2
#else
#define NUM_BACKWARD_TENSORS 3
#endif
typedef struct {
floatX* bt4c; // (B, T, 4*C)
floatX* preatt; // (B, NH, T, T)
floatX* residual3; // (B, T, C)
#ifndef ENABLE_CUDNN
floatX* preatt; // (B, NH, T, T)
#endif
} GradActTensors;
void fill_in_grad_act_sizes(size_t* act_sizes, size_t B, size_t T, GPT2Config config) {
size_t NH = config.num_heads;
size_t C = config.channels;
act_sizes[0] = B * T * 4 * C; // bt4c
act_sizes[1] = B * NH * T * T; // preatt
act_sizes[2] = B * T * C; // residual3
act_sizes[1] = B * T * C; // residual3
#ifndef ENABLE_CUDNN
size_t NH = config.num_heads;
act_sizes[2] = B * NH * T * T; // preatt
#endif
}
void* malloc_and_point(floatX** targets[], const size_t* act_sizes, size_t n) {
@ -1502,7 +1785,10 @@ void* malloc_and_point_activations(ActivationTensors* acts, const size_t* act_si
void* malloc_and_point_backward(GradActTensors* acts, const size_t* act_sizes) {
floatX** ptrs[] = {
&acts->bt4c, &acts->preatt, &acts->residual3
&acts->bt4c, &acts->residual3,
#ifndef ENABLE_CUDNN
&acts->preatt,
#endif
};
return malloc_and_point(ptrs, act_sizes, NUM_BACKWARD_TENSORS);
}
@ -1708,14 +1994,21 @@ void gpt2_forward(GPT2 *model, int* inputs, int* targets, size_t B, size_t T) {
floatX* l_fch_gelu = acts.fch_gelu + l * B * T * 4*C;
floatX* l_fcproj = acts.fcproj + l * B * T * C;
floatX* l_residual3 = acts.residual3 + l * B * T * C;
// these are only needed as scratchpads for the forward pass, but
// need not be stored for backward
floatX* scratch = (floatX*)acts.output;
// now do the forward pass
layernorm_forward(l_ln1, l_ln1_mean, l_ln1_rstd, residual, l_ln1w, l_ln1b, B, T, C);
#ifdef ENABLE_CUDNN
matmul_forward_cublaslt(l_qkvr, l_ln1, l_qkvw, l_qkvb, B, T, C, 3*C);
attention_forward_cudnn(l_atty, (float*)l_att, l_qkvr, B, T, NH, C);
#else
// these are only needed as scratchpads for the forward pass, but
// need not be stored for backward
floatX* scratch = (floatX*)acts.output;
matmul_forward_cublaslt(scratch, l_ln1, l_qkvw, l_qkvb, B, T, C, 3*C);
attention_forward(l_atty, l_qkvr, l_att, scratch, B, T, C, NH);
#endif
matmul_forward_cublaslt(l_attproj, l_atty, l_attprojw, l_attprojb, B, T, C, C);
residual_forward(l_residual2, residual, l_attproj, B*T*C);
layernorm_forward(l_ln2, l_ln2_mean, l_ln2_rstd, l_residual2, l_ln2w, l_ln2b, B, T, C);
@ -1794,9 +2087,8 @@ void gpt2_backward(GPT2 *model) {
ActivationTensors acts = model->acts;
GradActTensors grads_acts = model->grads_acts;
// re-use scratch buffer of the forward pass
// re-use the output buffer of the forward pass as a scratchpad during backward pass
float* scratchF = (float*)acts.output;
floatX* scratchX = (floatX*)acts.output;
// we kick off the chain rule by filling in dlosses with 1.0f/(B*T)
// this was done in the fused classifier kernel as last step of forward pass
@ -1854,7 +2146,6 @@ void gpt2_backward(GPT2 *model) {
// so we can co-opt it here.
floatX* dl_btc = (floatX*)acts.lnf;
floatX* dl_bt4c = (floatX*)grads_acts.bt4c;
floatX* dl_preatt = (floatX*)grads_acts.preatt;
// backprop this layer
matmul_backward(dl_bt4c, dl_fcprojw, dl_fcprojb, dresidual, l_fch_gelu, l_fcprojw, B, T, 4*C, C);
@ -1863,11 +2154,19 @@ void gpt2_backward(GPT2 *model) {
// layernorm backward does += to the dresidual, so it correctly accumulates grad from the MLP block above
layernorm_backward(dresidual, dl_ln2w, dl_ln2b, scratchF, dl_btc, l_residual2, l_ln2w, l_ln2_mean, l_ln2_rstd, B, T, C);
matmul_backward(dl_btc, dl_attprojw, dl_attprojb, dresidual, l_atty, l_attprojw, B, T, C, C);
// we more B x T x (4)C buffers. l_atty and l_fch aren't needed anymore at this point, so reuse their memory
#ifdef ENABLE_CUDNN
attention_backward_cudnn(dl_bt4c, dl_btc, l_qkvr, l_atty, (float*)l_att, B, T, NH, C);
#else
// we need B x T x (4)C buffers. l_atty and l_fch aren't needed anymore at this point, so reuse their memory
floatX* buffer_a = l_atty;
floatX* buffer_b = l_fch; // this is B x T x 4C, so even larger than what we need
floatX* dl_preatt = (floatX*)grads_acts.preatt; // dedicated scratchpad allocation
floatX* scratchX = (floatX*)acts.output;
attention_backward(dl_bt4c, buffer_b, dl_preatt, scratchX, buffer_a, dl_btc, l_qkvr, l_att, B, T, C, NH);
#endif
// QKV parameter gradients
matmul_backward(dl_btc, dl_qkvw, dl_qkvb, dl_bt4c, l_ln1, l_qkvw, B, T, C, 3 * C);
// layernorm backward does += to dresidual, so it correctly accumulates gradient for the Attention block above
layernorm_backward(dresidual, dl_ln1w, dl_ln1b, scratchF, dl_btc, residual, l_ln1w, l_ln1_mean, l_ln1_rstd, B, T, C);
@ -2155,6 +2454,10 @@ int main(int argc, char *argv[]) {
cublasCheck(cublasSetMathMode(cublas_handle, cublas_math_mode));
if(cublas_compute_type); // unused in BF16 mode, avoid warning
#ifdef ENABLE_CUDNN
checkCudnnErr(cudnnCreate(&cudnn_handle));
#endif
printf0("| device | %-50s |\n", deviceProp.name);
printf0("| TF32 | %-50s |\n", enable_tf32 ? "enabled" : "disabled");
printf0("| precision | %-50s |\n", precision_mode_str);
@ -2294,14 +2597,17 @@ int main(int argc, char *argv[]) {
cudaCheck(cudaDeviceSynchronize()); // finish all CUDA work to get correct precise timings
clock_gettime(CLOCK_MONOTONIC, &end);
double time_elapsed_s = (end.tv_sec - start.tv_sec) + (end.tv_nsec - start.tv_nsec) / 1e9;
total_sum_iteration_time_s += time_elapsed_s;
if (step > 0) { // consider the first batch to be a warmup (e.g. cuBLAS/cuDNN initialisation)
total_sum_iteration_time_s += time_elapsed_s;
}
int tokens_per_second = multi_gpu_config.num_processes * (B * T) / time_elapsed_s;
float accumulated_loss = multi_gpu_config.num_processes == 1 ? model.mean_loss : model.accumulated_mean_loss;
printf0("step %4d/%d: train loss %f (acc %f) (%f ms, %d tok/s)\n", step + 1, train_num_batches, model.mean_loss, accumulated_loss, time_elapsed_s * 1000, tokens_per_second);
logger_log_train(&logger, step, model.mean_loss);
}
// add a total average, for optimizations that are only mild improvements
printf0("total average iteration time: %f ms\n", total_sum_iteration_time_s / train_num_batches * 1000);
// add a total average, for optimizations that are only mild improvements (excluding 1st batch as warmup)
printf0("total average iteration time: %f ms\n", total_sum_iteration_time_s / (train_num_batches-1) * 1000);
// free
dataloader_free(&train_loader);
@ -2311,6 +2617,13 @@ int main(int argc, char *argv[]) {
free(cpu_logits_raw);
free(cpu_logits);
free(gen_tokens);
#ifdef ENABLE_CUDNN
if (cudnn_workspace_size > 0) {
cudaCheck(cudaFree(cudnn_workspace));
}
#endif
cudaCheck(cudaFree(cublaslt_workspace));
cublasCheck(cublasDestroy(cublas_handle));
cublasCheck(cublasLtDestroy(cublaslt_handle));