vbatts/llama.cpp
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mtl : add silu kernel

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This commit is contained in:
Georgi Gerganov 2023-06-01 21:35:11 +03:00
parent a0cc3de59a
commit 42dca4004c
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GPG key ID: 449E073F9DC10735
3 changed files with 37 additions and 5 deletions
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24
examples/mtl/mtl.m
View file

@ -30,6 +30,9 @@ struct ggml_mtl_context {
id<MTLFunction> function_scale; id<MTLFunction> function_scale;
id<MTLComputePipelineState> pipeline_scale; id<MTLComputePipelineState> pipeline_scale;
id<MTLFunction> function_silu;
id<MTLComputePipelineState> pipeline_silu;
id<MTLFunction> function_relu; id<MTLFunction> function_relu;
id<MTLComputePipelineState> pipeline_relu; id<MTLComputePipelineState> pipeline_relu;
@ -148,6 +151,10 @@ struct ggml_mtl_context * llama_mtl_init(
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);
ctx->function_silu = [ctx->library newFunctionWithName:@"kernel_silu"];
ctx->pipeline_silu = [ctx->device newComputePipelineStateWithFunction:ctx->function_silu error:nil];
fprintf(stderr, "%s: loaded kernel_silu: %p\n", __func__, (void *) ctx->pipeline_silu);
ctx->function_relu = [ctx->library newFunctionWithName:@"kernel_relu"]; ctx->function_relu = [ctx->library newFunctionWithName:@"kernel_relu"];
ctx->pipeline_relu = [ctx->device newComputePipelineStateWithFunction:ctx->function_relu error:nil]; ctx->pipeline_relu = [ctx->device newComputePipelineStateWithFunction:ctx->function_relu error:nil];
fprintf(stderr, "%s: loaded kernel_relu: %p\n", __func__, (void *) ctx->pipeline_relu); fprintf(stderr, "%s: loaded kernel_relu: %p\n", __func__, (void *) ctx->pipeline_relu);
@ -357,6 +364,23 @@ int llama_mtl_eval(
const int64_t n = ggml_nelements(gf->nodes[i]); const int64_t n = ggml_nelements(gf->nodes[i]);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_SILU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
id<MTLBuffer> id_src0 = llama_mtl_get_buffer(ctx, gf->nodes[i]->src0, &offs_src0);
id<MTLBuffer> id_dst = llama_mtl_get_buffer(ctx, gf->nodes[i], &offs_dst);
[encoder setComputePipelineState:ctx->pipeline_silu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(gf->nodes[i]);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break; } break;
case GGML_OP_RELU: case GGML_OP_RELU:

8
examples/mtl/mtl.metal
View file

@ -58,6 +58,14 @@ kernel void kernel_scale(
dst[tpig] = src0[tpig] * scale; dst[tpig] = src0[tpig] * scale;
} }
kernel void kernel_silu(
device const float * src0,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
float x = src0[tpig];
dst[tpig] = x / (1.0f + exp(-x));
}
kernel void kernel_relu( kernel void kernel_relu(
device const float * src0, device const float * src0,
device float * dst, device float * dst,

10
llama.cpp
View file

@ -1371,11 +1371,6 @@ static bool llama_eval_internal(
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
ggml_set_name(cur, "KQV_merged_contiguous"); ggml_set_name(cur, "KQV_merged_contiguous");
// TODO: TMP !!!!
if (il == 0) {
ggml_set_name(cur, "mtl-check");
}
// projection (no bias) // projection (no bias)
cur = ggml_mul_mat(ctx0, cur = ggml_mul_mat(ctx0,
model.layers[il].wo, model.layers[il].wo,
@ -1407,6 +1402,11 @@ static bool llama_eval_internal(
// SILU activation // SILU activation
cur = ggml_silu(ctx0, cur); cur = ggml_silu(ctx0, cur);
// TODO: TMP !!!!
if (il == 0) {
ggml_set_name(cur, "mtl-check");
}
cur = ggml_mul(ctx0, cur, tmp); cur = ggml_mul(ctx0, cur, tmp);
cur = ggml_mul_mat(ctx0, cur = ggml_mul_mat(ctx0,