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Overlap cmdbuffer creation and cmdbuffer execution in Vulkan backend …
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…by submitting smaller cmdbuffers early.
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@mtavenrath
mtavenrath committed Aug 21, 2024
1 parent fc54ef0 commit 73beb8d
Showing 1 changed file with 61 additions and 44 deletions.
105 changes: 61 additions & 44 deletions ggml/src/ggml-vulkan.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -785,6 +785,9 @@ static vk_submission ggml_vk_create_submission(vk_device& device, vk_queue& q, s

static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) {
if (ctx->seqs.empty()) {
if (fence) {
ctx->q->queue.submit({}, fence);
}
return;
}
VK_LOG_DEBUG("ggml_vk_submit(" << ctx << ", " << fence << ")");
Expand Down Expand Up @@ -5614,11 +5617,15 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
}
}

static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, int node_idx, bool last_node, bool dryrun){
bool ggml_vk_compute_forward(ggml_backend_vk_context* ctx, ggml_tensor* tensor, int tensor_idx, bool use_fence);

// Returns true if node has enqueued work into the queue, false otherwise
// If submit is true the current all operations queued so far are being submitted to Vulkan to overlap cmdlist creation and GPU execution.
static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, int node_idx, ggml_tensor *node_begin, int node_idx_begin, bool dryrun, bool submit){
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra;

if (ggml_is_empty(node) || extra == nullptr) {
return;
return false;
}

VK_LOG_DEBUG("ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")");
Expand All @@ -5635,7 +5642,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
case GGML_OP_NONE:
return;
return false;
case GGML_OP_UNARY:
switch (ggml_get_unary_op(node)) {
case GGML_UNARY_OP_SILU:
Expand All @@ -5645,7 +5652,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_UNARY_OP_TANH:
break;
default:
return;
return false;
}
break;
case GGML_OP_REPEAT:
Expand Down Expand Up @@ -5680,7 +5687,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
default:
std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl;
GGML_ABORT("fatal error");
return;
return false;
}

vk_context compute_ctx;
Expand Down Expand Up @@ -5772,7 +5779,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
ggml_vk_unary(ctx, compute_ctx, src0, node, dryrun);
break;
default:
return;
return false;
}
break;
case GGML_OP_DIAG_MASK_INF:
Expand Down Expand Up @@ -5816,11 +5823,11 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod

break;
default:
return;
return false;
}

if (dryrun) {
return;
return false;
}

ctx->tensor_ctxs[node_idx] = compute_ctx;
Expand All @@ -5831,14 +5838,26 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
last_node = true;
#endif

if (last_node) {
if (submit) {
ggml_vk_ctx_end(compute_ctx);
compute_ctx->exit_tensor_idx = node_idx;
ctx->compute_ctx.reset();

bool ok = ggml_vk_compute_forward(ctx, node_begin, node_idx_begin, false);
if (!ok) {
if (node->op == GGML_OP_UNARY) {
std::cerr << __func__ << ": error: op not supported UNARY " << node->name << " (" << ggml_unary_op_name(static_cast<ggml_unary_op>(node->op_params[0])) << ")" << std::endl;
}
else {
std::cerr << __func__ << ": error: op not supported " << node->name << " (" << ggml_op_name(node->op) << ")" << std::endl;
}
}

}
return true;
}

static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * tensor, int tensor_idx){
static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * tensor, int tensor_idx, bool use_fence = true){
ggml_tensor_extra_gpu * extra = nullptr;

switch (tensor->op) {
Expand Down Expand Up @@ -5910,9 +5929,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor *

vk_context subctx = ctx->tensor_ctxs[tensor_idx].lock();

#ifdef GGML_VULKAN_PERF
std::chrono::steady_clock::time_point start;
#endif // GGML_VULKAN_PERF
VkFence fence = use_fence ? ctx->fence : VkFence{};

// Only run if ctx hasn't been submitted yet
if (!subctx->seqs.empty()) {
Expand All @@ -5921,20 +5938,13 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor *
memcpy(cpy.dst, cpy.src, cpy.n);
}

#ifdef GGML_VULKAN_PERF
start = std::chrono::steady_clock::now();
#endif // GGML_VULKAN_PERF

ggml_vk_submit(subctx, ctx->fence);
ggml_vk_submit(subctx, fence);
}

if (tensor_idx == subctx->exit_tensor_idx) {
VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences");
if (tensor_idx != 0 && tensor_idx == subctx->exit_tensor_idx) {
ggml_vk_submit(subctx, fence);

#ifdef GGML_VULKAN_PERF
auto duration = std::chrono::duration_cast<std::chrono::nanoseconds>(std::chrono::steady_clock::now() - start);
ctx->device->perf_logger->log_timing(tensor, duration.count());
#endif // GGML_VULKAN_PERF
VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences");

ctx->device->device.resetFences({ ctx->fence });

Expand Down Expand Up @@ -6426,7 +6436,7 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen
ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;

for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_vk_build_graph(ctx, cgraph->nodes[i], i, 0, true);
ggml_vk_build_graph(ctx, cgraph->nodes[i], i, nullptr, 0, true, false);
}
ggml_vk_preallocate_buffers(ctx);
ggml_pipeline_allocate_descriptor_sets(ctx->device);
Expand All @@ -6441,33 +6451,40 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen
// Reserve tensor context space for all nodes
ctx->tensor_ctxs.resize(cgraph->n_nodes);

for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_vk_build_graph(ctx, cgraph->nodes[i], i, i == last_node, false);
}
bool first_node_in_batch = true; // true if next node will be first node in a batch
int submit_node_idx = 0; // index to first node in a batch

// submit work every submit_count node to overlap CPU cmdbuffer generation with GPU execution
constexpr int submit_count = 50;
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];

if (ggml_vk_is_empty(node)) {
continue;
if (first_node_in_batch) {
submit_node_idx = i;
}

bool submit = ((i % submit_count) == 0) || (i == last_node);
bool enqueued = ggml_vk_build_graph(ctx, cgraph->nodes[i], i, cgraph->nodes[submit_node_idx], submit_node_idx, false, submit);

bool ok = ggml_vk_compute_forward(ctx, node, i);
if (!ok) {
if (node->op == GGML_OP_UNARY) {
std::cerr << __func__ << ": error: op not supported UNARY " << node->name << " (" << ggml_unary_op_name(static_cast<ggml_unary_op>(node->op_params[0])) << ")" << std::endl;
} else {
std::cerr << __func__ << ": error: op not supported " << node->name << " (" << ggml_op_name(node->op) << ")" << std::endl;
}
if (first_node_in_batch && enqueued) {
first_node_in_batch = false;
}
#ifdef GGML_VULKAN_CHECK_RESULTS
else {
ggml_vk_check_results_1(node);
if (submit) {
first_node_in_batch = true;
}
#endif
GGML_ASSERT(ok);
}

// wait for work on the GPU to complete work
bool ok = ggml_vk_compute_forward(ctx, cgraph->nodes[cgraph->n_nodes-1], cgraph->n_nodes - 1, true);

if (!ok) {
std::cerr << __func__ << ": error: failed to enqueue cmdbuffer" << std::endl;
}
#ifdef GGML_VULKAN_CHECK_RESULTS
else {
ggml_vk_check_results_1(node);
}
#endif
GGML_ASSERT(ok);

#ifdef GGML_VULKAN_PERF
ctx->device->perf_logger->print_timings();
#endif
Expand Down

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