replica
/
llama.cpp
mirror of https://github.com/ggml-org/llama.cpp.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

metal : Add missing unary ops Metal support (#14660)

This commit is contained in:
Yavor Ivanov 2025-07-12 22:38:13 -07:00 committed by GitHub
parent 84b396e051
commit dcf7f2ea3c
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
2 changed files with 135 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

90
ggml/src/ggml-metal/ggml-metal.m
View File

@ -173,6 +173,12 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_SILU, GGML_METAL_KERNEL_TYPE_SILU,
GGML_METAL_KERNEL_TYPE_SILU_4, GGML_METAL_KERNEL_TYPE_SILU_4,
GGML_METAL_KERNEL_TYPE_ELU, GGML_METAL_KERNEL_TYPE_ELU,
GGML_METAL_KERNEL_TYPE_ABS,
GGML_METAL_KERNEL_TYPE_SGN,
GGML_METAL_KERNEL_TYPE_STEP,
GGML_METAL_KERNEL_TYPE_HARDSWISH,
GGML_METAL_KERNEL_TYPE_HARDSIGMOID,
GGML_METAL_KERNEL_TYPE_EXP,
GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16,
GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4,
GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32,
@ -1155,6 +1161,12 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4, silu_4, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4, silu_4, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ELU, elu, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ELU, elu, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ABS, abs, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SGN, sgn, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_STEP, step, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_HARDSWISH, hardswish, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_HARDSIGMOID, hardsigmoid, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_EXP, exp, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, soft_max_f16, has_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, soft_max_f16, has_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, soft_max_f16_4, has_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, soft_max_f16_4, has_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, soft_max_f32, has_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, soft_max_f32, has_simdgroup_reduction);
@ -1688,6 +1700,12 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_SILU:
case GGML_UNARY_OP_ELU: case GGML_UNARY_OP_ELU:
case GGML_UNARY_OP_NEG: case GGML_UNARY_OP_NEG:
case GGML_UNARY_OP_ABS:
case GGML_UNARY_OP_SGN:
case GGML_UNARY_OP_STEP:
case GGML_UNARY_OP_HARDSWISH:
case GGML_UNARY_OP_HARDSIGMOID:
case GGML_UNARY_OP_EXP:
return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
default: default:
return false; return false;
@ -2439,6 +2457,78 @@ static bool ggml_metal_encode_node(
[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_UNARY_OP_ABS:
{
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ABS].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_UNARY_OP_SGN:
{
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SGN].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_UNARY_OP_STEP:
{
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_STEP].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_UNARY_OP_HARDSWISH:
{
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_HARDSWISH].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_UNARY_OP_HARDSIGMOID:
{
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_HARDSIGMOID].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_UNARY_OP_EXP:
{
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_EXP].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
default: default:
{ {
GGML_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op)); GGML_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op));

45
ggml/src/ggml-metal/ggml-metal.metal
View File

@ -1199,6 +1199,51 @@ kernel void kernel_neg(
dst[tpig] = -src0[tpig]; dst[tpig] = -src0[tpig];
} }
kernel void kernel_abs(
device const float * src0,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = fabs(src0[tpig]);
}
kernel void kernel_sgn(
device const float * src0,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
device const float & x = src0[tpig];
dst[tpig] = (x > 0.0f) ? 1.0f : ((x < 0.0f) ? -1.0f : 0.0f);
}
kernel void kernel_step(
device const float * src0,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = src0[tpig] > 0.0f ? 1.0f : 0.0f;
}
kernel void kernel_hardswish(
device const float * src0,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
device const float & x = src0[tpig];
dst[tpig] = x * fmin(1.0f, fmax(0.0f, (x + 3.0f) / 6.0f));
}
kernel void kernel_hardsigmoid(
device const float * src0,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
device const float & x = src0[tpig];
dst[tpig] = fmin(1.0f, fmax(0.0f, (x + 3.0f) / 6.0f));
}
kernel void kernel_exp(
device const float * src0,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = exp(src0[tpig]);
}
kernel void kernel_reglu( kernel void kernel_reglu(
device const char * src0, device const char * src0,
device const char * src1, device const char * src1,