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Fall back if graph capture fails and address other comments

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This commit is contained in:
Alan Gray 2024-04-30 06:19:51 -07:00
parent 909e4c664b
commit 58199503a8
2 changed files with 77 additions and 37 deletions
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108
ggml-cuda.cu
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@ -48,11 +48,20 @@
static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
[[noreturn]] static bool disable_cuda_graphs_due_to_failed_capture = false;
void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg) { void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg) {
int id = -1; // in case cudaGetDevice fails int id = -1; // in case cudaGetDevice fails
cudaGetDevice(&id); cudaGetDevice(&id);
if(strcmp(msg,"operation not permitted when stream is capturing")==0 ||
strcmp(msg,"operation failed due to a previous error during capture")==0) {
// CUDA graph capture has failed, but we can fall back to regular stream-based CUDA
// so mark as failed, clear the error and return.
disable_cuda_graphs_due_to_failed_capture = true;
cudaGetLastError();
return;
}
fprintf(stderr, "CUDA error: %s\n", msg); fprintf(stderr, "CUDA error: %s\n", msg);
fprintf(stderr, " current device: %d, in function %s at %s:%d\n", id, func, file, line); fprintf(stderr, " current device: %d, in function %s at %s:%d\n", id, func, file, line);
fprintf(stderr, " %s\n", stmt); fprintf(stderr, " %s\n", stmt);
@ -2428,6 +2437,7 @@ struct ggml_cuda_graph {
cudaKernelNodeParams params[MAX_NODES_IN_CUDA_GRAPH]; cudaKernelNodeParams params[MAX_NODES_IN_CUDA_GRAPH];
bool disable_due_to_gpu_arch = false; bool disable_due_to_gpu_arch = false;
bool disable_due_to_too_many_updates = false; bool disable_due_to_too_many_updates = false;
bool disable_due_to_failed_graph_capture = false;
int number_consecutive_updates = 0; int number_consecutive_updates = 0;
ggml_graph_node_properties ggml_graph_properties[MAX_NODES_IN_CUDA_GRAPH]; ggml_graph_node_properties ggml_graph_properties[MAX_NODES_IN_CUDA_GRAPH];
}; };
@ -2436,26 +2446,28 @@ struct ggml_cuda_graph {
const bool disable_cuda_graphs = (getenv("LLAMACPP_DISABLE_CUDA_GRAPHS") != nullptr); const bool disable_cuda_graphs = (getenv("LLAMACPP_DISABLE_CUDA_GRAPHS") != nullptr);
GGML_CALL static void set_ggml_graph_node_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) { GGML_CALL static void set_ggml_graph_node_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
graph_node_properties->node_address = node; graph_node_properties->node_address = node->data;
graph_node_properties->node_op = node->op; graph_node_properties->node_op = node->op;
for(int i=0; i<GGML_MAX_DIMS; i++) { for(int i=0; i<GGML_MAX_DIMS; i++) {
graph_node_properties->ne[i] = node->ne[i]; graph_node_properties->ne[i] = node->ne[i];
graph_node_properties->nb[i] = node->nb[i]; graph_node_properties->nb[i] = node->nb[i];
} }
for(int i=0; i<GGML_MAX_SRC; i++) { for(int i=0; i<GGML_MAX_SRC; i++) {
graph_node_properties->src_address[i] = node->src[i]; graph_node_properties->src_address[i] = node->src[i] ? node->src[i]->data : nullptr;
} }
} }
GGML_CALL static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) { GGML_CALL static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
if(node != graph_node_properties->node_address) return false; if(node->data != graph_node_properties->node_address &&
node->op != GGML_OP_CPY && node->op != GGML_OP_VIEW) return false;
if(node->op != graph_node_properties->node_op) return false; if(node->op != graph_node_properties->node_op) return false;
for(int i=0; i<GGML_MAX_DIMS; i++) { for(int i=0; i<GGML_MAX_DIMS; i++) {
if(node->ne[i] != graph_node_properties->ne[i]) return false; if(node->ne[i] != graph_node_properties->ne[i]) return false;
if(node->nb[i] != graph_node_properties->nb[i]) return false; if(node->nb[i] != graph_node_properties->nb[i]) return false;
} }
for(int i=0; i<GGML_MAX_SRC; i++) { for(int i=0; i<GGML_MAX_SRC; i++) {
if(node->src[i] != graph_node_properties->src_address[i]) return false; if(node->src[i] && node->src[i]->data != graph_node_properties->src_address[i] &&
node->op != GGML_OP_CPY && node->op != GGML_OP_VIEW) return false;
} }
return true; return true;
} }
@ -2467,46 +2479,54 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
#ifdef USE_CUDA_GRAPH #ifdef USE_CUDA_GRAPH
// Objects required for CUDA Graph // Objects required for CUDA Graph
static ggml_cuda_graph cuda_graph; if(cuda_ctx->cuda_graph == nullptr)
bool use_cuda_graph = (cuda_graph.count >= 7); //avoid CUDA graphs on first few steps due to incompatible initialisations. {
cuda_ctx->cuda_graph = (ggml_cuda_graph *) malloc(sizeof(ggml_cuda_graph));
}
bool use_cuda_graph = (cuda_ctx->cuda_graph->count >= 7); //avoid CUDA graphs on first few steps due to incompatible initialisations.
char ** updated_kernel_arg[MAX_NODES_IN_CUDA_GRAPH]; char ** updated_kernel_arg[MAX_NODES_IN_CUDA_GRAPH];
bool cuda_graph_update_required = false; bool cuda_graph_update_required = false;
// pointer to CUDA cpy kernel, which is required to identify // pointer to CUDA cpy kernel, which is 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; void * ggml_cuda_cpy_fn_ptr = nullptr;
if(cuda_graph.count == 0){ if(cuda_ctx->cuda_graph->count == 0){
if (ggml_cuda_info().devices[cuda_ctx->device].cc < 800){ if (ggml_cuda_info().devices[cuda_ctx->device].cc < 800){
cuda_graph.disable_due_to_gpu_arch=true; cuda_ctx->cuda_graph->disable_due_to_gpu_arch=true;
} }
} }
// Disable CUDA graphs in presence of env var, old GPU or use-case which is changing too rapidly. // Disable CUDA graphs in presence of env var, old GPU, use-case which is changing too rapidly,
// or previous graph capture failure.
// Also disable for multi-gpu for now. TO DO investigate // Also disable for multi-gpu for now. TO DO investigate
if(disable_cuda_graphs || cuda_graph.disable_due_to_gpu_arch || cuda_graph.disable_due_to_too_many_updates || if(disable_cuda_graphs || cuda_ctx->cuda_graph->disable_due_to_gpu_arch ||
cuda_ctx->cuda_graph->disable_due_to_too_many_updates || cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture ||
ggml_backend_cuda_get_device_count() > 1){ ggml_backend_cuda_get_device_count() > 1){
use_cuda_graph = false; use_cuda_graph = false;
} }
if(use_cuda_graph) { if(use_cuda_graph) {
if(cuda_graph.instance == nullptr) cuda_graph_update_required=true; if(cuda_ctx->cuda_graph->instance == nullptr) cuda_graph_update_required=true;
// Loop over nodes in GGML graph to determine if CUDA graph update is required // Loop over nodes in GGML graph to determine if CUDA graph update is required
// and store properties to allow this comparison for the next token // and store properties to allow this comparison for the next token
for (int i = 0; i < cgraph->n_nodes; i++) { for (int i = 0; i < cgraph->n_nodes; i++) {
bool has_matching_properties = true; bool has_matching_properties = true;
if(!cuda_graph_update_required) { if(!cuda_graph_update_required) {
has_matching_properties = ggml_graph_node_has_matching_properties(cgraph->nodes[i], &cuda_graph.ggml_graph_properties[i]); has_matching_properties = ggml_graph_node_has_matching_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
} }
if(!has_matching_properties) cuda_graph_update_required = true; if(!has_matching_properties) cuda_graph_update_required = true;
set_ggml_graph_node_properties(cgraph->nodes[i], &cuda_graph.ggml_graph_properties[i]); set_ggml_graph_node_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
} }
// Loop over nodes in GGML graph to obtain info needed for CUDA graph // Loop over nodes in GGML graph to obtain info needed for CUDA graph
int k=0; int k=0;
for (int i = 0; i < cgraph->n_nodes; i++) { for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i]; ggml_tensor * node = cgraph->nodes[i];
if(node->op == GGML_OP_MUL_MAT_ID) {
use_cuda_graph = false; // This node type is not supported by CUDA graph capture
}
if(node->op == GGML_OP_SOFT_MAX) { if(node->op == GGML_OP_SOFT_MAX) {
if(node->src[1]->ne[1] > 1){ if(node->src[1]->ne[1] > 1){
use_cuda_graph = false; // disable CUDA graphs for batch size > 1 for now. TO DO investigate use_cuda_graph = false; // disable CUDA graphs for batch size > 1 for now. TO DO investigate
@ -2524,12 +2544,12 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
// Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates. // Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates.
if(cuda_graph_update_required) { if(cuda_graph_update_required) {
cuda_graph.number_consecutive_updates++; cuda_ctx->cuda_graph->number_consecutive_updates++;
} }
else { else {
cuda_graph.number_consecutive_updates = 0; cuda_ctx->cuda_graph->number_consecutive_updates = 0;
} }
if (cuda_graph.number_consecutive_updates >= 4) cuda_graph.disable_due_to_too_many_updates = true; if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true;
} }
if(use_cuda_graph && cuda_graph_update_required) { // Start CUDA graph capture if(use_cuda_graph && cuda_graph_update_required) { // Start CUDA graph capture
@ -2540,11 +2560,16 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
bool use_cuda_graph = false; bool use_cuda_graph = false;
bool cuda_graph_update_required = false; bool cuda_graph_update_required = false;
#endif // USE_CUDA_GRAPH #endif // USE_CUDA_GRAPH
bool graph_evaluated_or_captured = false;
while(!graph_evaluated_or_captured) {
// Temporarily avoid indenting here (and below the following if) to make code review easier
// Only perform the graph execution if CUDA graphs are not enabled, or we are capturing the graph. // Only perform the graph execution if CUDA graphs are not enabled, or we are capturing the graph.
// With the use of CUDA graphs, the execution will be performed by the graph launch. // With the use of CUDA graphs, the execution will be performed by the graph launch.
if(!use_cuda_graph || cuda_graph_update_required) { if(!use_cuda_graph || cuda_graph_update_required) {
//temporarily avoid indenting here to make code review easier
for (int i = 0; i < cgraph->n_nodes; i++) { for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i]; ggml_tensor * node = cgraph->nodes[i];
@ -2571,12 +2596,23 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
#ifdef USE_CUDA_GRAPH #ifdef USE_CUDA_GRAPH
if(use_cuda_graph && (cuda_graph_update_required)) { // End CUDA graph capture if(use_cuda_graph && (cuda_graph_update_required)) { // End CUDA graph capture
CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_graph.graph)); CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_ctx->cuda_graph->graph));
if(disable_cuda_graphs_due_to_failed_capture) {
use_cuda_graph = false;
cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true;
}
else {
graph_evaluated_or_captured = true; // CUDA graph has been captured
}
}
else {
graph_evaluated_or_captured = true; // ggml graph has been directly evaluated
}
} }
if(use_cuda_graph){ if(use_cuda_graph){
if(cuda_graph.instance == nullptr) { // Create executable graph from captured graph. if(cuda_ctx->cuda_graph->instance == nullptr) { // Create executable graph from captured graph.
CUDA_CHECK(cudaGraphInstantiate(&cuda_graph.instance, cuda_graph.graph, NULL, NULL, 0)); CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
} }
@ -2584,19 +2620,19 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
if(cuda_graph_update_required) { if(cuda_graph_update_required) {
// Extract nodes from graph // Extract nodes from graph
if(cuda_graph.num_nodes == 0) { if(cuda_ctx->cuda_graph->num_nodes == 0) {
// First call with null argument gets number of nodes in graph // First call with null argument gets number of nodes in graph
CUDA_CHECK(cudaGraphGetNodes(cuda_graph.graph, nullptr, &cuda_graph.num_nodes)); CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, nullptr, &cuda_ctx->cuda_graph->num_nodes));
} }
// Subsequent call with non-null argument gets nodes // Subsequent call with non-null argument gets nodes
CUDA_CHECK(cudaGraphGetNodes(cuda_graph.graph, cuda_graph.nodes, &cuda_graph.num_nodes)); CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, cuda_ctx->cuda_graph->nodes, &cuda_ctx->cuda_graph->num_nodes));
// Loop over nodes, and extract kernel parameters from each node // Loop over nodes, and extract kernel parameters from each node
for(size_t i=0; i<cuda_graph.num_nodes; i++) { for(size_t i=0; i<cuda_ctx->cuda_graph->num_nodes; i++) {
cudaGraphNodeType node_type; cudaGraphNodeType node_type;
CUDA_CHECK(cudaGraphNodeGetType(cuda_graph.nodes[i], &node_type)); CUDA_CHECK(cudaGraphNodeGetType(cuda_ctx->cuda_graph->nodes[i], &node_type));
if (node_type == cudaGraphNodeTypeKernel) { if (node_type == cudaGraphNodeTypeKernel) {
auto stat = cudaGraphKernelNodeGetParams(cuda_graph.nodes[i], &cuda_graph.params[i]); // Get params using runtime auto stat = cudaGraphKernelNodeGetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]); // Get params using runtime
if(stat == cudaErrorInvalidDeviceFunction) { if(stat == cudaErrorInvalidDeviceFunction) {
// Fails due to incorrect handling by CUDA runtime of CUDA BLAS node. // Fails due to incorrect handling by CUDA runtime of CUDA BLAS node.
// We don't need to update blas nodes, so clear error and move on. // We don't need to update blas nodes, so clear error and move on.
@ -2613,31 +2649,31 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
// replace that argument with the updated value in the CUDA graph // replace that argument with the updated value in the CUDA graph
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_graph.num_nodes; i++) { for(size_t i=0; i<cuda_ctx->cuda_graph->num_nodes; i++) {
if(cuda_graph.params[i].func == ggml_cuda_cpy_fn_ptr) { if(cuda_ctx->cuda_graph->params[i].func == ggml_cuda_cpy_fn_ptr) {
char ** updated_kernel_arg_ptr = updated_kernel_arg[k++]; char ** updated_kernel_arg_ptr = updated_kernel_arg[k++];
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_graph.nodes[i], &cuda_graph.params[i])); CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]));
} }
} }
} }
// Update graph executable // Update graph executable
cudaGraphExecUpdateResultInfo result_info; cudaGraphExecUpdateResultInfo result_info;
auto stat = cudaGraphExecUpdate(cuda_graph.instance, cuda_graph.graph, &result_info); auto stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info);
if(stat == cudaErrorGraphExecUpdateFailure) { if(stat == cudaErrorGraphExecUpdateFailure) {
// The pre-existing graph exec cannot be updated due to violated constraints // The pre-existing graph exec cannot be updated due to violated constraints
// so instead clear error and re-instantiate // so instead clear error and re-instantiate
cudaGetLastError(); cudaGetLastError();
CUDA_CHECK(cudaGraphInstantiate(&cuda_graph.instance, cuda_graph.graph, NULL, NULL, 0)); CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
} }
else { else {
GGML_ASSERT(stat == cudaSuccess); GGML_ASSERT(stat == cudaSuccess);
} }
// Launch graph // Launch graph
CUDA_CHECK(cudaGraphLaunch(cuda_graph.instance, cuda_ctx->stream())); CUDA_CHECK(cudaGraphLaunch(cuda_ctx->cuda_graph->instance, cuda_ctx->stream()));
} }
cuda_graph.count++; cuda_ctx->cuda_graph->count++;
#endif // USE_CUDA_GRAPH #endif // USE_CUDA_GRAPH
return GGML_STATUS_SUCCESS; return GGML_STATUS_SUCCESS;
} }

6
ggml-cuda/common.cuh
View file

@ -172,7 +172,6 @@
#define GGML_CUDA_MAX_STREAMS 8 #define GGML_CUDA_MAX_STREAMS 8
[[noreturn]]
void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg); void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg);
#define CUDA_CHECK_GEN(err, success, error_fn) \ #define CUDA_CHECK_GEN(err, success, error_fn) \
@ -479,6 +478,8 @@ struct ggml_tensor_extra_gpu {
cudaEvent_t events[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS]; // events for synchronizing multiple GPUs cudaEvent_t events[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS]; // events for synchronizing multiple GPUs
}; };
struct ggml_cuda_graph;
struct ggml_backend_cuda_context { struct ggml_backend_cuda_context {
int device; int device;
std::string name; std::string name;
@ -487,6 +488,8 @@ struct ggml_backend_cuda_context {
cudaStream_t streams[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS] = { { nullptr } }; cudaStream_t streams[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS] = { { nullptr } };
cublasHandle_t cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr}; cublasHandle_t cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
ggml_cuda_graph * cuda_graph = nullptr;
explicit ggml_backend_cuda_context(int device) : explicit ggml_backend_cuda_context(int device) :
device(device), device(device),
name(GGML_CUDA_NAME + std::to_string(device)) { name(GGML_CUDA_NAME + std::to_string(device)) {
@ -506,6 +509,7 @@ struct ggml_backend_cuda_context {
CUBLAS_CHECK(cublasDestroy(cublas_handles[i])); CUBLAS_CHECK(cublasDestroy(cublas_handles[i]));
} }
} }
if(cuda_graph != nullptr) free(cuda_graph);
} }
cudaStream_t stream(int device, int stream) { cudaStream_t stream(int device, int stream) {