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cuda : synchronize graph capture and cublas handle destruction (llama/14288)

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Workarounds an issue that may cause CUDA graph capture to fail when a cuBLAS handle is destroyed in a different thread
This commit is contained in:
Diego Devesa
2025-06-20 04:57:36 -07:00
committed by Georgi Gerganov
parent 018b2d340e
commit 33d1f0a3e0
2 changed files with 43 additions and 19 deletions
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18
ggml/src/ggml-cuda/common.cuh
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@ -19,10 +19,10 @@
#endif
#include "ggml-common.h"
#include <cstdio>
#include <array>
#include <cassert>
#include <cfloat>
#include <cstdio>
#include <string>
#include <vector>
@ -767,21 +767,7 @@ struct ggml_backend_cuda_context {
name(GGML_CUDA_NAME + std::to_string(device)) {
}
~ggml_backend_cuda_context() {
if (copy_event != nullptr) {
CUDA_CHECK(cudaEventDestroy(copy_event));
}
for (int i = 0; i < GGML_CUDA_MAX_DEVICES; ++i) {
for (int j = 0; j < GGML_CUDA_MAX_STREAMS; ++j) {
if (streams[i][j] != nullptr) {
CUDA_CHECK(cudaStreamDestroy(streams[i][j]));
}
}
if (cublas_handles[i] != nullptr) {
CUBLAS_CHECK(cublasDestroy(cublas_handles[i]));
}
}
}
~ggml_backend_cuda_context();
cudaStream_t stream(int device, int stream) {
if (streams[device][stream] == nullptr) {

44
ggml/src/ggml-cuda/ggml-cuda.cu
View File

@ -48,6 +48,7 @@
#include <atomic>
#include <charconv>
#include <cinttypes>
#include <condition_variable>
#include <cstddef>
#include <cstdint>
#include <float.h>
@ -55,9 +56,8 @@
#include <map>
#include <memory>
#include <mutex>
#include <stdint.h>
#include <stdio.h>
#include <stdarg.h>
#include <stdio.h>
#include <stdlib.h>
#include <string>
#include <vector>
@ -515,6 +515,33 @@ std::unique_ptr<ggml_cuda_pool> ggml_backend_cuda_context::new_pool_for_device(i
return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_leg(device));
}
// destroying a cuBLAS handle while a graph is being captured in a different thread can result in a CUDA error
// this lock is used to ensure that no cuBLAS handle is destroyed while a graph is being captured
static std::mutex ggml_cuda_lock;
static std::condition_variable ggml_cuda_lock_cv;
static std::atomic<int> ggml_cuda_lock_counter;
ggml_backend_cuda_context::~ggml_backend_cuda_context() {
std::unique_lock<std::mutex> lock(ggml_cuda_lock);
ggml_cuda_lock_cv.wait(lock, []{ return ggml_cuda_lock_counter.load(std::memory_order_relaxed) == 0; });
if (copy_event != nullptr) {
CUDA_CHECK(cudaEventDestroy(copy_event));
}
for (int i = 0; i < GGML_CUDA_MAX_DEVICES; ++i) {
for (int j = 0; j < GGML_CUDA_MAX_STREAMS; ++j) {
if (streams[i][j] != nullptr) {
CUDA_CHECK(cudaStreamDestroy(streams[i][j]));
}
}
if (cublas_handles[i] != nullptr) {
CUBLAS_CHECK(cublasDestroy(cublas_handles[i]));
}
}
}
// cuda buffer
struct ggml_backend_cuda_buffer_context {
@ -2689,6 +2716,11 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_ctx->cuda_graph->graph));
graph_evaluated_or_captured = true; // CUDA graph has been captured
std::lock_guard<std::mutex> lock(ggml_cuda_lock);
if (ggml_cuda_lock_counter.fetch_sub(1, std::memory_order_relaxed) == 1) {
ggml_cuda_lock_cv.notify_all();
}
} else {
graph_evaluated_or_captured = true; // ggml graph has been directly evaluated
}
@ -2764,7 +2796,13 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
}
}
if (use_cuda_graph && cuda_graph_update_required) { // Start CUDA graph capture
if (use_cuda_graph && cuda_graph_update_required) {
// Start CUDA graph capture
{
std::lock_guard<std::mutex> lock(ggml_cuda_lock);
ggml_cuda_lock_counter.fetch_add(1, std::memory_order_relaxed);
}
CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed));
}