vbatts/llama.cpp
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mtl : add non-broadcast mul kernel

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This commit is contained in:
Georgi Gerganov 2023-06-01 21:40:53 +03:00
parent 42dca4004c
commit fbd3f6258d
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GPG key ID: 449E073F9DC10735
3 changed files with 27 additions and 4 deletions
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17
examples/mtl/mtl.m
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@ -27,6 +27,10 @@ struct ggml_mtl_context {
id<MTLFunction> function_mul; id<MTLFunction> function_mul;
id<MTLComputePipelineState> pipeline_mul; id<MTLComputePipelineState> pipeline_mul;
// TODO: avoid this extra kernel, instead extend the "mul" kernel to support broadcast
id<MTLFunction> function_mul_row;
id<MTLComputePipelineState> pipeline_mul_row;
id<MTLFunction> function_scale; id<MTLFunction> function_scale;
id<MTLComputePipelineState> pipeline_scale; id<MTLComputePipelineState> pipeline_scale;
@ -147,6 +151,10 @@ struct ggml_mtl_context * llama_mtl_init(
ctx->pipeline_mul = [ctx->device newComputePipelineStateWithFunction:ctx->function_mul error:nil]; ctx->pipeline_mul = [ctx->device newComputePipelineStateWithFunction:ctx->function_mul error:nil];
fprintf(stderr, "%s: loaded kernel_mul: %p\n", __func__, (void *) ctx->pipeline_mul); fprintf(stderr, "%s: loaded kernel_mul: %p\n", __func__, (void *) ctx->pipeline_mul);
ctx->function_mul_row = [ctx->library newFunctionWithName:@"kernel_mul_row"];
ctx->pipeline_mul_row = [ctx->device newComputePipelineStateWithFunction:ctx->function_mul_row error:nil];
fprintf(stderr, "%s: loaded kernel_mul_row: %p\n", __func__, (void *) ctx->pipeline_mul_row);
ctx->function_scale = [ctx->library newFunctionWithName:@"kernel_scale"]; ctx->function_scale = [ctx->library newFunctionWithName:@"kernel_scale"];
ctx->pipeline_scale = [ctx->device newComputePipelineStateWithFunction:ctx->function_scale error:nil]; ctx->pipeline_scale = [ctx->device newComputePipelineStateWithFunction:ctx->function_scale error:nil];
fprintf(stderr, "%s: loaded kernel_scale: %p\n", __func__, (void *) ctx->pipeline_scale); fprintf(stderr, "%s: loaded kernel_scale: %p\n", __func__, (void *) ctx->pipeline_scale);
@ -336,7 +344,14 @@ int llama_mtl_eval(
const int64_t ne00 = gf->nodes[i]->src0->ne[0]; const int64_t ne00 = gf->nodes[i]->src0->ne[0];
[encoder setComputePipelineState:ctx->pipeline_mul]; const int64_t ne10 = gf->nodes[i]->src1->ne[0];
if (ggml_nelements(gf->nodes[i]->src1) == ne10) {
// src1 is a row
[encoder setComputePipelineState:ctx->pipeline_mul_row];
} else {
[encoder setComputePipelineState:ctx->pipeline_mul];
}
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2];

10
examples/mtl/mtl.metal
View file

@ -39,9 +39,17 @@ kernel void kernel_add(
dst[tpig] = src0[tpig] + src1[tpig]; dst[tpig] = src0[tpig] + src1[tpig];
} }
kernel void kernel_mul(
device const float * src0,
device const float * src1,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = src0[tpig] * src1[tpig];
}
// assumption: src1 is a row // assumption: src1 is a row
// broadcast src1 into src0 // broadcast src1 into src0
kernel void kernel_mul( kernel void kernel_mul_row(
device const float * src0, device const float * src0,
device const float * src1, device const float * src1,
device float * dst, device float * dst,

4
llama.cpp
View file

@ -1402,13 +1402,13 @@ static bool llama_eval_internal(
// SILU activation // SILU activation
cur = ggml_silu(ctx0, cur); cur = ggml_silu(ctx0, cur);
cur = ggml_mul(ctx0, cur, tmp);
// TODO: TMP !!!! // TODO: TMP !!!!
if (il == 0) { if (il == 0) {
ggml_set_name(cur, "mtl-check"); ggml_set_name(cur, "mtl-check");
} }
cur = ggml_mul(ctx0, cur, tmp);
cur = ggml_mul_mat(ctx0, cur = ggml_mul_mat(ctx0,
model.layers[il].w2, model.layers[il].w2,
cur); cur);