aditya/llama.cpp
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metal : add f16 support

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This commit is contained in:
Georgi Gerganov 2023-06-06 20:16:57 +03:00
parent d5b111f53d
commit 44f906e853
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GPG key ID: 449E073F9DC10735
3 changed files with 31 additions and 11 deletions
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23
ggml-metal.m
View file

@ -47,10 +47,11 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(relu); GGML_METAL_DECL_KERNEL(relu);
GGML_METAL_DECL_KERNEL(soft_max); GGML_METAL_DECL_KERNEL(soft_max);
GGML_METAL_DECL_KERNEL(diag_mask_inf); GGML_METAL_DECL_KERNEL(diag_mask_inf);
GGML_METAL_DECL_KERNEL(get_rows_f16);
GGML_METAL_DECL_KERNEL(get_rows_q4_0); GGML_METAL_DECL_KERNEL(get_rows_q4_0);
GGML_METAL_DECL_KERNEL(rms_norm); GGML_METAL_DECL_KERNEL(rms_norm);
GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_DECL_KERNEL(mul_mat_f16_f32); GGML_METAL_DECL_KERNEL(mul_mat_f16_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_DECL_KERNEL(rope); GGML_METAL_DECL_KERNEL(rope);
GGML_METAL_DECL_KERNEL(cpy_f32_f16); GGML_METAL_DECL_KERNEL(cpy_f32_f16);
GGML_METAL_DECL_KERNEL(cpy_f32_f32); GGML_METAL_DECL_KERNEL(cpy_f32_f32);
@ -130,10 +131,11 @@ struct ggml_metal_context * ggml_metal_init(void) {
GGML_METAL_ADD_KERNEL(relu); GGML_METAL_ADD_KERNEL(relu);
GGML_METAL_ADD_KERNEL(soft_max); GGML_METAL_ADD_KERNEL(soft_max);
GGML_METAL_ADD_KERNEL(diag_mask_inf); GGML_METAL_ADD_KERNEL(diag_mask_inf);
GGML_METAL_ADD_KERNEL(get_rows_f16);
GGML_METAL_ADD_KERNEL(get_rows_q4_0); GGML_METAL_ADD_KERNEL(get_rows_q4_0);
GGML_METAL_ADD_KERNEL(rms_norm); GGML_METAL_ADD_KERNEL(rms_norm);
GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_ADD_KERNEL(mul_mat_f16_f32); GGML_METAL_ADD_KERNEL(mul_mat_f16_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_ADD_KERNEL(rope); GGML_METAL_ADD_KERNEL(rope);
GGML_METAL_ADD_KERNEL(cpy_f32_f16); GGML_METAL_ADD_KERNEL(cpy_f32_f16);
GGML_METAL_ADD_KERNEL(cpy_f32_f32); GGML_METAL_ADD_KERNEL(cpy_f32_f32);
@ -498,6 +500,14 @@ void ggml_metal_graph_compute(
// use custom matrix x vector kernel // use custom matrix x vector kernel
switch (src0t) { switch (src0t) {
case GGML_TYPE_F16:
{
GGML_ASSERT(ne02 == ne12);
nth0 = 64;
nth1 = 1;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
} break;
case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_0:
{ {
GGML_ASSERT(ne02 == 1); GGML_ASSERT(ne02 == 1);
@ -507,14 +517,6 @@ void ggml_metal_graph_compute(
nth1 = 4; nth1 = 4;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_0_f32]; [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_0_f32];
} break; } break;
case GGML_TYPE_F16:
{
GGML_ASSERT(ne02 == ne12);
nth0 = 32;
nth1 = 1;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
} break;
default: GGML_ASSERT(false && "not implemented"); default: GGML_ASSERT(false && "not implemented");
}; };
@ -551,6 +553,7 @@ void ggml_metal_graph_compute(
} }
switch (src0->type) { switch (src0->type) {
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break;
case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break; case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
default: GGML_ASSERT(false && "not implemented"); default: GGML_ASSERT(false && "not implemented");
} }

16
ggml-metal.metal
View file

@ -169,6 +169,22 @@ kernel void kernel_diag_mask_inf(
} }
} }
kernel void kernel_get_rows_f16(
device const void * src0,
device const int * src1,
device float * dst,
constant int64_t & ne00,
constant uint64_t & nb01,
constant uint64_t & nb1,
uint tpig[[thread_position_in_grid]]) {
const int i = tpig;
const int r = ((device int32_t *) src1)[i];
for (int j = 0; j < ne00; j++) {
dst[i*nb1 + j] = ((device half *) ((device char *) src0 + r*nb01))[j];
}
}
kernel void kernel_get_rows_q4_0( kernel void kernel_get_rows_q4_0(
device const void * src0, device const void * src0,
device const int * src1, device const int * src1,

3
llama.cpp
View file

@ -961,7 +961,6 @@ static void llama_model_load_internal(
model.hparams = ml->file_loaders.at(0)->hparams; model.hparams = ml->file_loaders.at(0)->hparams;
llama_file_version file_version = ml->file_loaders.at(0)->file_version; llama_file_version file_version = ml->file_loaders.at(0)->file_version;
auto & hparams = model.hparams; auto & hparams = model.hparams;
uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
{ {
switch (hparams.n_layer) { switch (hparams.n_layer) {
@ -975,6 +974,8 @@ static void llama_model_load_internal(
hparams.n_ctx = n_ctx; hparams.n_ctx = n_ctx;
} }
const uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
{ {
fprintf(stderr, "%s: format = %s\n", __func__, llama_file_version_name(file_version)); fprintf(stderr, "%s: format = %s\n", __func__, llama_file_version_name(file_version));
fprintf(stderr, "%s: n_vocab = %u\n", __func__, hparams.n_vocab); fprintf(stderr, "%s: n_vocab = %u\n", __func__, hparams.n_vocab);