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Allow multiple copy function pointers for CUDA graph kernel param updates (#7565)

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CUDA graphs require parameter updates to kernels associated with
GGML_OP_CPY nodes. Previously the implementation only checked for a
single CUDA kernel in such nodes, but this caused a bug in cases where
2 such kernels exist. This fixes the issue by using a vector to allow
multiple function pointers to be stored and checked against.

Fixes #7942
This commit is contained in:
agray3 2024-05-27 18:33:42 +01:00 committed by GitHub
parent 95f84d5ce8
commit 197c00681b
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1 changed files with 7 additions and 6 deletions
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13
ggml-cuda.cu
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@ -2510,9 +2510,9 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
bool use_cuda_graph = true; bool use_cuda_graph = true;
bool cuda_graph_update_required = false; bool cuda_graph_update_required = false;
// pointer to CUDA cpy kernel, which is required to identify // vector of pointers to CUDA cpy kernels, which are required to identify
// kernel parameters which need updated in the graph for each token // kernel parameters which need updated in the graph for each token
void * ggml_cuda_cpy_fn_ptr = nullptr; std::vector<void *> ggml_cuda_cpy_fn_ptrs;
if (cuda_ctx->cuda_graph->graph == nullptr) { if (cuda_ctx->cuda_graph->graph == nullptr) {
if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) { if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) {
@ -2588,9 +2588,10 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
if (node->op == GGML_OP_CPY) { if (node->op == GGML_OP_CPY) {
// store the copy op parameter which changes with each token. // store the copy op parameter which changes with each token.
cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data)); cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data));
if (ggml_cuda_cpy_fn_ptr == nullptr) { // store a pointer to each copy op CUDA kernel to identify it later
// store a pointer to the copy op CUDA kernel to identify it later void * ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]);
ggml_cuda_cpy_fn_ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]); if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) {
ggml_cuda_cpy_fn_ptrs.push_back(ptr);
} }
} }
@ -2720,7 +2721,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured
int k = 0; int k = 0;
for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) { for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
if (cuda_ctx->cuda_graph->params[i].func == ggml_cuda_cpy_fn_ptr) { if(count(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), cuda_ctx->cuda_graph->params[i].func) > 0) {
char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++); char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++);
cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr; cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr;
CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i])); CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]));