mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-10-30 06:30:15 +01:00
metal : concurrently dispatch commands (#2358)
* metal: concurrently dispatch commands Function `ggml_metal_graph_find_concurrency` will run and write commands that can be issued concurrently to metal context `concur_list` array, when `ggml_metal_graph_compute` is called for the first time. * metal: don't call find_concurrency automatically. * metal : code style changes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Shouzheng Liu
GitHub
parent
9a08eaf3c4
commit
1aa18ef994
@ -61,6 +61,13 @@ void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor *
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// get data from the device into host memory
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void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
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// try to find operations that can be run concurrently in the graph
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// you should run it again if the topology of your graph changes
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void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
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// if the graph has been optimized for concurrently dispatch
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bool ggml_metal_if_optimized(struct ggml_metal_context * ctx);
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// same as ggml_graph_compute but uses Metal
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// creates gf->n_threads command buffers in parallel
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void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
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145
ggml-metal.m
145
ggml-metal.m
@ -36,6 +36,9 @@ struct ggml_metal_context {
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int n_buffers;
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struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
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int concur_list[GGML_MAX_NODES];
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int concur_list_len;
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// custom kernels
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#define GGML_METAL_DECL_KERNEL(name) \
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id<MTLFunction> function_##name; \
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@ -98,6 +101,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
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ctx->device = MTLCreateSystemDefaultDevice();
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ctx->queue = [ctx->device newCommandQueue];
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ctx->n_buffers = 0;
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ctx->concur_list_len = 0;
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// determine if we can use MPS
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if (MPSSupportsMTLDevice(ctx->device)) {
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@ -217,6 +221,13 @@ void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) {
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ctx->n_cb = n_cb;
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}
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bool ggml_metal_if_optimized(struct ggml_metal_context * ctx) {
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if (ctx->concur_list_len) {
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return true;
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}
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return false;
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}
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// finds the Metal buffer that contains the tensor data on the GPU device
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// the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the
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// Metal buffer based on the host memory pointer
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@ -355,11 +366,98 @@ void ggml_metal_get_tensor(
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memcpy(t->data, (void *) ((uint8_t *) id_src.contents + offs), ggml_nbytes(t));
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}
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void ggml_metal_graph_find_concurrency(
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struct ggml_metal_context * ctx,
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struct ggml_cgraph * gf) {
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int search_depth = gf->n_nodes; //we only find concurrency in this range to avoid wasting too much time
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int nodes_unused[GGML_MAX_NODES];
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for (int i = 0; i < GGML_MAX_NODES; i++) {ctx->concur_list[i] = 0;}
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for (int i = 0; i < gf->n_nodes; i++) {nodes_unused[i] = 1;}
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ctx->concur_list_len = 0;
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int n_left = gf->n_nodes;
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int n_start = 0; // all nodes before n_start at nodes_unused array have been sorted and store back to ctx->concur_list
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int level_pos = 0; // at ctx->concur_list, the last layer (level) ends at level_pos
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while (n_left > 0) {
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// number of nodes at a layer (that can be issued concurrently)
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int concurrency = 0;
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for (int i = n_start; i < ((n_start + search_depth > gf->n_nodes) ? gf->n_nodes : n_start + search_depth); i++) {
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if (nodes_unused[i]) {
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// if the requirements for gf->nodes[i] are satisfied
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int exe_flag=1;
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// scan all srcs
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for (int src_ind = 0; src_ind < GGML_MAX_SRC; src_ind++) {
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struct ggml_tensor * src_cur = gf->nodes[i]->src[src_ind];
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if (src_cur) {
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// if is leaf nodes it's satisfied.
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if (src_cur->op == GGML_OP_NONE && src_cur->grad == NULL) {continue;}
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// otherwise this src should be the output from previous nodes.
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int is_found = 0;
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// scan 2*search_depth back because we inserted barrier.
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for (int j = ((level_pos - 2*search_depth) < 0 ? 0 : (level_pos - 2*search_depth)); j < level_pos; j++) {
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if (gf->nodes[ctx->concur_list[j]] == src_cur) {is_found = 1; break;}
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}
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if (is_found == 0) {exe_flag = 0; break;}
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}
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}
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if (exe_flag) {
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// check if nodes[i]'s data will be overwritten by a node before nodes[i].
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// if node[5] and node[3] write to the same memory region, then we can't issue node[5] before node[3]
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int64_t data_start = (int64_t) gf->nodes[i]->data;
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int64_t length = (int64_t) ggml_nbytes(gf->nodes[i]);
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for (int j = n_start; j < i; j++) {
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if (nodes_unused[j] && gf->nodes[j]->op != GGML_OP_RESHAPE \
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&& gf->nodes[j]->op != GGML_OP_VIEW \
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&& gf->nodes[j]->op != GGML_OP_TRANSPOSE \
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&& gf->nodes[j]->op != GGML_OP_PERMUTE) {
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if (((int64_t)gf->nodes[j]->data) >= data_start + length || \
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((int64_t)gf->nodes[j]->data) + (int64_t) ggml_nbytes(gf->nodes[j]) <= data_start) {
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continue;
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} else {
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exe_flag = 0;
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}
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}
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}
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}
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if (exe_flag) {
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ctx->concur_list[level_pos + concurrency] = i;
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nodes_unused[i] = 0;
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concurrency++;
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ctx->concur_list_len++;
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}
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}
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}
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n_left -= concurrency;
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// adding a barrier different layer
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ctx->concur_list[level_pos + concurrency] = -1;
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ctx->concur_list_len++;
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// jump all sorted nodes at nodes_bak
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while (!nodes_unused[n_start]) {n_start++;}
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level_pos += concurrency + 1;
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}
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if (ctx->concur_list_len > GGML_MAX_NODES) {
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fprintf(stderr, "%s: too many elements for metal ctx->concur_list!\n", __func__);
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}
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}
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void ggml_metal_graph_compute(
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struct ggml_metal_context * ctx,
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struct ggml_cgraph * gf) {
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metal_printf("%s: evaluating graph\n", __func__);
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// if there is ctx->concur_list, dispatch concurrently
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// else fallback to serial dispatch
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MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor;
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const bool has_concur = ctx->concur_list_len && ctx->concur_list_len <= GGML_MAX_NODES;
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const int n_nodes = has_concur ? ctx->concur_list_len : gf->n_nodes;
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edesc.dispatchType = has_concur ? MTLDispatchTypeConcurrent : MTLDispatchTypeSerial;
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// create multiple command buffers and enqueue them
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// then, we encode the graph into the command buffers in parallel
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@ -378,7 +476,7 @@ void ggml_metal_graph_compute(
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dispatch_queue_t queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
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for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
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const int n_nodes_per_cb = (gf->n_nodes + n_cb - 1) / n_cb;
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const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb;
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dispatch_async(queue, ^{
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size_t offs_src0 = 0;
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@ -390,9 +488,20 @@ void ggml_metal_graph_compute(
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id<MTLComputeCommandEncoder> encoder = nil;
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const int node_start = (cb_idx + 0) * n_nodes_per_cb;
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const int node_end = (cb_idx == n_cb - 1) ? gf->n_nodes : (cb_idx + 1) * n_nodes_per_cb;
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const int node_end = (cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb;
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for (int ind = node_start; ind < node_end; ++ind) {
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const int i = has_concur ? ctx->concur_list[ind] : ind;
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if (i == -1) {
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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continue;
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}
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[encoder memoryBarrierWithScope:MTLBarrierScopeBuffers];
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continue;
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}
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for (int i = node_start; i < node_end; ++i) {
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metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
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struct ggml_tensor * src0 = gf->nodes[i]->src[0];
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@ -463,7 +572,7 @@ void ggml_metal_graph_compute(
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case GGML_OP_ADD:
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{
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoder];
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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}
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if (ggml_nelements(src1) == ne10) {
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@ -484,7 +593,7 @@ void ggml_metal_graph_compute(
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case GGML_OP_MUL:
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{
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoder];
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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}
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if (ggml_nelements(src1) == ne10) {
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@ -505,7 +614,7 @@ void ggml_metal_graph_compute(
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case GGML_OP_SCALE:
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{
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoder];
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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}
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const float scale = *(const float *) src1->data;
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@ -524,7 +633,7 @@ void ggml_metal_graph_compute(
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case GGML_UNARY_OP_SILU:
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{
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoder];
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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}
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[encoder setComputePipelineState:ctx->pipeline_silu];
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@ -538,7 +647,7 @@ void ggml_metal_graph_compute(
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case GGML_UNARY_OP_RELU:
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{
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoder];
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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}
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[encoder setComputePipelineState:ctx->pipeline_relu];
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@ -552,7 +661,7 @@ void ggml_metal_graph_compute(
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case GGML_UNARY_OP_GELU:
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{
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoder];
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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}
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[encoder setComputePipelineState:ctx->pipeline_gelu];
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@ -572,7 +681,7 @@ void ggml_metal_graph_compute(
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case GGML_OP_SOFT_MAX:
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{
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoder];
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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}
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const int nth = 32;
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@ -590,7 +699,7 @@ void ggml_metal_graph_compute(
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case GGML_OP_DIAG_MASK_INF:
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{
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoder];
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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}
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const int n_past = ((int32_t *)(dst->op_params))[0];
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@ -653,7 +762,7 @@ void ggml_metal_graph_compute(
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}
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} else {
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoder];
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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}
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int nth0 = 32;
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@ -780,7 +889,7 @@ void ggml_metal_graph_compute(
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case GGML_OP_GET_ROWS:
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{
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoder];
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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}
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switch (src0->type) {
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@ -809,7 +918,7 @@ void ggml_metal_graph_compute(
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case GGML_OP_RMS_NORM:
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{
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoder];
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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}
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float eps;
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@ -832,7 +941,7 @@ void ggml_metal_graph_compute(
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case GGML_OP_NORM:
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{
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoder];
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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}
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const float eps = 1e-5f;
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@ -854,7 +963,7 @@ void ggml_metal_graph_compute(
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case GGML_OP_ALIBI:
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{
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoder];
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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}
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GGML_ASSERT((src0t == GGML_TYPE_F32));
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@ -897,7 +1006,7 @@ void ggml_metal_graph_compute(
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case GGML_OP_ROPE:
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{
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoder];
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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}
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const int n_past = ((int32_t *) dst->op_params)[0];
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@ -941,7 +1050,7 @@ void ggml_metal_graph_compute(
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case GGML_OP_CONT:
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{
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if (encoder == nil) {
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encoder = [command_buffer computeCommandEncoder];
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encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
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}
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const int nth = 32;
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@ -1720,6 +1720,9 @@ static bool llama_eval_internal(
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#ifdef GGML_USE_METAL
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if (lctx.ctx_metal && N == 1) {
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if (!ggml_metal_if_optimized(lctx.ctx_metal)) {
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ggml_metal_graph_find_concurrency(lctx.ctx_metal,&gf);
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}
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ggml_metal_set_n_cb (lctx.ctx_metal, n_threads);
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ggml_metal_graph_compute(lctx.ctx_metal, &gf);
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ggml_metal_get_tensor (lctx.ctx_metal, cur);
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