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Introduction of CUDA Graphs to LLama.cpp (#6766)

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* DRAFT: Introduction of CUDA Graphs to LLama.cpp

* FIx issues raised in comments

* Tidied to now only use CUDA runtime (not mixed with driver calls)

* disable for multi-gpu and batch size > 1

* Disable CUDA graphs for old GPU arch and with env var

* added missing CUDA_CHECKs

* Addressed comments

* further addressed comments

* limit to GGML_ALLOW_CUDA_GRAPHS defined in llama.cpp cmake

* Added more comprehensive graph node checking

* With mechanism to fall back if graph capture fails

* Revert "With mechanism to fall back if graph capture fails"

This reverts commit eb9f15fb6fcb81384f732c4601a5b25c016a5143.

* Fall back if graph capture fails and address other comments

* - renamed GGML_ALLOW_CUDA_GRAPHS to GGML_CUDA_USE_GRAPHS

- rename env variable to disable CUDA graphs to GGML_CUDA_DISABLE_GRAPHS

- updated Makefile build to enable CUDA graphs

- removed graph capture failure checking in ggml_cuda_error
  using a global variable to track this is not thread safe, but I am also not safistied with checking an error by string
  if this is necessary to workaround some issues with graph capture with eg. cuBLAS, we can pass the ggml_backend_cuda_context to the error checking macro and store the result in the context

- fixed several resource leaks

- fixed issue with zero node graphs

- changed fixed size arrays to vectors

- removed the count of number of evaluations before start capturing, and instead changed the capture mode to relaxed

- removed the check for multiple devices so that it is still possible to use a single device, instead checks for split buffers to disable cuda graphs with -sm row

- changed the op for checking batch size to GGML_OP_ADD, should be more reliable than GGML_OP_SOFT_MAX

- code style fixes

- things to look into
  - VRAM usage of the cudaGraphExec_t, if it is significant we may need to make it optional
  - possibility of using cudaStreamBeginCaptureToGraph to keep track of which ggml graph nodes correspond to which cuda graph nodes

* fix build without cuda graphs

* remove outdated comment

* replace minimum cc value with a constant

---------

Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
agray3 2024-05-08 21:55:49 +01:00 • committed by GitHub
parent c12452c7ae
commit bc4bba364f
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11 changed files with 372 additions and 44 deletions
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300
ggml-cuda.cu
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@ -1647,7 +1647,7 @@ static void ggml_cuda_op_mul_mat(
}
}
static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer));
GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
@ -1670,7 +1670,7 @@ static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const gg
ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream);
}
static void ggml_cuda_mul_mat_vec_nc(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
static void ggml_cuda_mul_mat_vec_nc(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(!ggml_is_transposed(src0));
GGML_ASSERT(!ggml_is_transposed(src1));
GGML_ASSERT(!ggml_is_permuted(src0));
@ -2410,32 +2410,304 @@ GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
GGML_UNUSED(backend);
}
static void set_ggml_graph_node_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
graph_node_properties->node_address = node->data;
graph_node_properties->node_op = node->op;
for (int i = 0; i < GGML_MAX_DIMS; i++) {
graph_node_properties->ne[i] = node->ne[i];
graph_node_properties->nb[i] = node->nb[i];
}
for (int i = 0; i < GGML_MAX_SRC; i++) {
graph_node_properties->src_address[i] = node->src[i] ? node->src[i]->data : nullptr;
}
}
static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
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;
}
for (int i = 0; i < GGML_MAX_DIMS; i++) {
if (node->ne[i] != graph_node_properties->ne[i]) {
return false;
}
if (node->nb[i] != graph_node_properties->nb[i]) {
return false;
}
}
for (int i = 0; i < GGML_MAX_SRC; i++) {
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;
}
GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
ggml_cuda_set_device(cuda_ctx->device);
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
#ifdef USE_CUDA_GRAPH
static const bool disable_cuda_graphs_due_to_env = (getenv("GGML_CUDA_DISABLE_GRAPHS") != nullptr);
if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
continue;
// Objects required for CUDA Graph
if (cuda_ctx->cuda_graph == nullptr) {
cuda_ctx->cuda_graph.reset(new ggml_cuda_graph());
}
bool use_cuda_graph = true;
bool cuda_graph_update_required = false;
// pointer to CUDA cpy kernel, which is required to identify
// kernel parameters which need updated in the graph for each token
void * ggml_cuda_cpy_fn_ptr = nullptr;
if (cuda_ctx->cuda_graph->graph == nullptr) {
if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) {
cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true;
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to GPU architecture\n", __func__);
#endif
}
}
// 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
if (disable_cuda_graphs_due_to_env
|| 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) {
use_cuda_graph = false;
}
if (use_cuda_graph) {
if (cuda_ctx->cuda_graph->instance == nullptr) {
cuda_graph_update_required = true;
}
// Check if the graph size has changed
if (cuda_ctx->cuda_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) {
cuda_graph_update_required = true;
cuda_ctx->cuda_graph->ggml_graph_properties.resize(cgraph->n_nodes);
}
// 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
for (int i = 0; i < cgraph->n_nodes; i++) {
bool has_matching_properties = true;
if (!cuda_graph_update_required) {
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;
}
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
cuda_ctx->cuda_graph->updated_kernel_arg.clear();
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
if (node->src[0] && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) {
use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture
#ifndef NDEBUG
assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
for (int j = 0; j < GGML_MAX_SRC; j++) {
if (node->src[j] != nullptr) {
assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer));
fprintf(stderr, "%s: disabling CUDA graphs due to split buffer\n", __func__);
#endif
}
if (node->op == GGML_OP_MUL_MAT_ID) {
use_cuda_graph = false; // This node type is not supported by CUDA graph capture
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
#endif
}
if (node->op == GGML_OP_ADD && node->src[1] && node->src[1]->ne[1] > 1) {
// disable CUDA graphs for batch size > 1 for now.
// Changes in batch size or context size can cause changes to the grid size of some kernels.
use_cuda_graph = false;
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
#endif
}
if (node->op == GGML_OP_CPY) {
// store the copy op parameter which changes with each token.
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 the copy op CUDA kernel to identify it later
ggml_cuda_cpy_fn_ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]);
}
}
if (!use_cuda_graph) {
break;
}
}
// Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates.
if (cuda_graph_update_required) {
cuda_ctx->cuda_graph->number_consecutive_updates++;
} else {
cuda_ctx->cuda_graph->number_consecutive_updates = 0;
}
if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) {
cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true;
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
#endif
}
}
if (use_cuda_graph && cuda_graph_update_required) { // Start CUDA graph capture
CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed));
}
#else
bool use_cuda_graph = false;
bool cuda_graph_update_required = false;
#endif // USE_CUDA_GRAPH
bool graph_evaluated_or_captured = false;
while (!graph_evaluated_or_captured) {
// 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.
if (!use_cuda_graph || cuda_graph_update_required) {
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
continue;
}
#ifndef NDEBUG
assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
for (int j = 0; j < GGML_MAX_SRC; j++) {
if (node->src[j] != nullptr) {
assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer));
}
}
#endif
bool ok = ggml_cuda_compute_forward(*cuda_ctx, node);
if (!ok) {
fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
bool ok = ggml_cuda_compute_forward(*cuda_ctx, node);
if (!ok) {
fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
}
GGML_ASSERT(ok);
}
}
GGML_ASSERT(ok);
#ifdef USE_CUDA_GRAPH
if (use_cuda_graph && cuda_graph_update_required) { // End CUDA graph capture
if (cuda_ctx->cuda_graph->graph != nullptr) {
CUDA_CHECK(cudaGraphDestroy(cuda_ctx->cuda_graph->graph));
cuda_ctx->cuda_graph->graph = nullptr;
}
CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_ctx->cuda_graph->graph));
#if 0
if (disable_cuda_graphs_due_to_failed_capture) {
use_cuda_graph = false;
cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true;
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to failed graph capture\n", __func__);
#endif
} else {
graph_evaluated_or_captured = true; // CUDA graph has been captured
}
#endif
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 (cuda_ctx->cuda_graph->instance == nullptr) { // Create executable graph from captured graph.
CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
}
// Perform update to graph (if required for this token), and change copy parameter (required for every token)
if (cuda_graph_update_required) {
// Extract nodes from graph
if (cuda_ctx->cuda_graph->num_nodes == 0) {
// First call with null argument gets number of nodes in graph
CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, nullptr, &cuda_ctx->cuda_graph->num_nodes));
}
// Subsequent call with non-null argument gets nodes
cuda_ctx->cuda_graph->nodes.resize(cuda_ctx->cuda_graph->num_nodes);
cuda_ctx->cuda_graph->params.resize(cuda_ctx->cuda_graph->num_nodes);
if (cuda_ctx->cuda_graph->num_nodes > 0) {
CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, cuda_ctx->cuda_graph->nodes.data(), &cuda_ctx->cuda_graph->num_nodes));
// Loop over nodes, and extract kernel parameters from each node
for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
cudaGraphNodeType node_type;
CUDA_CHECK(cudaGraphNodeGetType(cuda_ctx->cuda_graph->nodes[i], &node_type));
if (node_type == cudaGraphNodeTypeKernel) {
cudaError_t stat = cudaGraphKernelNodeGetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]); // Get params using runtime
if (stat == cudaErrorInvalidDeviceFunction) {
// 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.
cudaGetLastError();
} else {
GGML_ASSERT(stat == cudaSuccess);
}
}
}
}
}
// One of the arguments to the copy kernel is updated for each token, hence we need to
// 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
int k = 0;
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) {
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_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]));
}
}
}
// Update graph executable
cudaGraphExecUpdateResultInfo result_info;
cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info);
if (stat == cudaErrorGraphExecUpdateFailure) {
#ifndef NDEBUG
fprintf(stderr, "%s: CUDA graph update failed\n", __func__);
#endif
// The pre-existing graph exec cannot be updated due to violated constraints
// so instead clear error and re-instantiate
cudaGetLastError();
CUDA_CHECK(cudaGraphExecDestroy(cuda_ctx->cuda_graph->instance));
cuda_ctx->cuda_graph->instance = nullptr;
CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
} else {
GGML_ASSERT(stat == cudaSuccess);
}
// Launch graph
CUDA_CHECK(cudaGraphLaunch(cuda_ctx->cuda_graph->instance, cuda_ctx->stream()));
#else
graph_evaluated_or_captured = true;
#endif // USE_CUDA_GRAPH
}
return GGML_STATUS_SUCCESS;