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metal : support GGML_OP_SET #19548

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Merged
ggerganov merged 1 commit into from
Feb 13, 2026
+153 −9
Merged

metal : support GGML_OP_SET #19548

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1 change: 1 addition & 0 deletions ggml/src/ggml-metal/ggml-metal-device.m
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Original file line number Diff line number Diff line change
Expand Up @@ -1159,6 +1159,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID:
return has_simdgroup_reduction;
case GGML_OP_SET:
case GGML_OP_CPY:
case GGML_OP_DUP:
case GGML_OP_CONT:
Expand Down
132 changes: 132 additions & 0 deletions ggml/src/ggml-metal/ggml-metal-ops.cpp
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Expand Up @@ -426,6 +426,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
{
n_fuse = ggml_metal_op_flash_attn_ext(ctx, idx);
} break;
case GGML_OP_SET:
{
n_fuse = ggml_metal_op_set(ctx, idx);
} break;
case GGML_OP_DUP:
case GGML_OP_CPY:
case GGML_OP_CONT:
Expand Down Expand Up @@ -1609,6 +1613,134 @@ int ggml_metal_op_solve_tri(ggml_metal_op_t ctx, int idx) {
return 1;
}

int ggml_metal_op_set(ggml_metal_op_t ctx, int idx) {
ggml_tensor * op = ctx->node(idx);

ggml_metal_library_t lib = ctx->lib;
ggml_metal_encoder_t enc = ctx->enc;

GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
GGML_TENSOR_LOCALS( int32_t, ne1, op->src[1], ne);
GGML_TENSOR_LOCALS(uint64_t, nb1, op->src[1], nb);
GGML_TENSOR_LOCALS( int32_t, ne, op, ne);
GGML_TENSOR_LOCALS(uint64_t, nb, op, nb);

ggml_metal_buffer_id bid_src0 = ggml_metal_get_buffer_id(op->src[0]);
ggml_metal_buffer_id bid_src1 = ggml_metal_get_buffer_id(op->src[1]);
ggml_metal_buffer_id bid_dst = ggml_metal_get_buffer_id(op);

const size_t pnb1 = ((const int32_t *) op->op_params)[0];
const size_t pnb2 = ((const int32_t *) op->op_params)[1];
const size_t pnb3 = ((const int32_t *) op->op_params)[2];
const size_t offs = ((const int32_t *) op->op_params)[3];

const bool inplace = (bool) ((const int32_t *) op->op_params)[4];

if (!inplace) {
// run a separete kernel to cpy src->dst
// not sure how to avoid this
// TODO: make a simpler cpy_bytes kernel

//const id<MTLComputePipelineState> pipeline = ctx->pipelines[GGML_METAL_PIPELINE_TYPE_CPY_F32_F32].obj;
auto pipeline = ggml_metal_library_get_pipeline_cpy(lib, op->src[0]->type, op->type);

ggml_metal_kargs_cpy args = {
/*.nk0 =*/ ne00,
/*.ne00 =*/ ne00,
/*.ne01 =*/ ne01,
/*.ne02 =*/ ne02,
/*.ne03 =*/ ne03,
/*.nb00 =*/ nb00,
/*.nb01 =*/ nb01,
/*.nb02 =*/ nb02,
/*.nb03 =*/ nb03,
/*.ne0 =*/ ne0,
/*.ne1 =*/ ne1,
/*.ne2 =*/ ne2,
/*.ne3 =*/ ne3,
/*.nb0 =*/ nb0,
/*.nb1 =*/ nb1,
/*.nb2 =*/ nb2,
/*.nb3 =*/ nb3,
};

ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
ggml_metal_encoder_set_buffer (enc, bid_src0, 1);
ggml_metal_encoder_set_buffer (enc, bid_dst, 2);

const int nth = std::min(ggml_metal_pipeline_max_theads_per_threadgroup(pipeline), ne00);

ggml_metal_encoder_dispatch_threadgroups(enc, ne01, ne02, ne03, nth, 1, 1);

ggml_metal_op_concurrency_reset(ctx);
}

auto pipeline = ggml_metal_library_get_pipeline_cpy(lib, op->src[1]->type, op->type);

GGML_ASSERT(ne10 % ggml_blck_size(op->src[1]->type) == 0);

int64_t nk0 = ne10;
if (ggml_is_quantized(op->src[1]->type)) {
nk0 = ne10/16;
} else if (ggml_is_quantized(op->type)) {
nk0 = ne10/ggml_blck_size(op->type);
}

int nth = std::min<int>(nk0, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));

// when rows are small, we can batch them together in a single threadgroup
int nrptg = 1;

// TODO: relax this constraint in the future
if (ggml_blck_size(op->src[1]->type) == 1 && ggml_blck_size(op->type) == 1) {
if (nth > nk0) {
nrptg = (nth + nk0 - 1)/nk0;
nth = nk0;

if (nrptg*nth > ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
nrptg--;
}
}
}

nth = std::min<int>(nth, nk0);

ggml_metal_kargs_cpy args = {
/*.nk0 =*/ nk0,
/*.ne00 =*/ ne10,
/*.ne01 =*/ ne11,
/*.ne02 =*/ ne12,
/*.ne03 =*/ ne13,
/*.nb00 =*/ nb10,
/*.nb01 =*/ nb11,
/*.nb02 =*/ nb12,
/*.nb03 =*/ nb13,
/*.ne0 =*/ ne10,
/*.ne1 =*/ ne11,
/*.ne2 =*/ ne12,
/*.ne3 =*/ ne13,
/*.nb0 =*/ ggml_element_size(op),
/*.nb1 =*/ pnb1,
/*.nb2 =*/ pnb2,
/*.nb3 =*/ pnb3,
};

const int nw0 = nrptg == 1 ? (nk0 + nth - 1)/nth : 1;

bid_dst.offs += offs;

ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
ggml_metal_encoder_set_buffer (enc, bid_src1, 1);
ggml_metal_encoder_set_buffer (enc, bid_dst, 2);

ggml_metal_encoder_dispatch_threadgroups(enc, nw0*(ne11 + nrptg - 1)/nrptg, ne12, ne13, nth, nrptg, 1);

return 1;
}

int ggml_metal_op_cpy(ggml_metal_op_t ctx, int idx) {
ggml_tensor * op = ctx->node(idx);

Expand Down
1 change: 1 addition & 0 deletions ggml/src/ggml-metal/ggml-metal-ops.h
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Expand Up @@ -59,6 +59,7 @@ int ggml_metal_op_ssm_conv (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_ssm_scan (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_rwkv (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_solve_tri (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_set (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_cpy (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_pool_1d (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_pool_2d (ggml_metal_op_t ctx, int idx);
Expand Down
28 changes: 19 additions & 9 deletions tests/test-backend-ops.cpp
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Expand Up @@ -2786,18 +2786,19 @@ struct test_set : public test_case {
const ggml_type type_dst;
const std::array<int64_t, 4> ne;
const int dim;
const bool inplace;

std::string vars() override {
return VARS_TO_STR4(type_src, type_dst, ne, dim);
return VARS_TO_STR5(type_src, type_dst, ne, dim, inplace);
}

size_t op_size(ggml_tensor * t) override {
return ggml_nbytes(t) + ggml_nbytes(t->src[0]);
}

test_set(ggml_type type_src = GGML_TYPE_F32, ggml_type type_dst = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {6, 5, 4, 3}, int dim = 1)
: type_src(type_src), type_dst(type_dst), ne(ne), dim(dim) {}
std::array<int64_t, 4> ne = {6, 5, 4, 3}, int dim = 1, bool inplace = false)
: type_src(type_src), type_dst(type_dst), ne(ne), dim(dim), inplace(inplace) {}

ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data());
Expand All @@ -2808,17 +2809,24 @@ struct test_set : public test_case {
for (int i = 0; i < dim; ++i) {
ne_dst[i] *= 2;
}
ggml_tensor* dst = ggml_new_tensor(ctx, type_dst, 4, ne_dst.data());
ggml_tensor * dst = ggml_new_tensor(ctx, type_dst, 4, ne_dst.data());
ggml_set_param(dst);
ggml_set_name(dst, "dst");

size_t offset = 0;
for (int i = 0; i < dim; ++i) {
offset += ((ne_dst[i] - ne[i])/2)*dst->nb[i];
}
ggml_tensor * out = ggml_set(ctx, dst, src,
// The backward pass requires setting a contiguous region:
src->nb[1], src->nb[2], src->nb[3], offset);
ggml_tensor * out;
if (inplace) {
out = ggml_set_inplace(ctx, dst, src,
// The backward pass requires setting a contiguous region:
src->nb[1], src->nb[2], src->nb[3], offset);
} else {
out = ggml_set(ctx, dst, src,
// The backward pass requires setting a contiguous region:
src->nb[1], src->nb[2], src->nb[3], offset);
}
ggml_set_name(out, "out");

return out;
Expand Down Expand Up @@ -7428,11 +7436,13 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {1, 2, 0, 3}));

for (int dim = 1; dim < GGML_MAX_DIMS; ++dim) {
test_cases.emplace_back(new test_set(GGML_TYPE_F32, GGML_TYPE_F32, {6, 5, 4, 3}, dim));
test_cases.emplace_back(new test_set(GGML_TYPE_F32, GGML_TYPE_F32, {6, 5, 4, 3}, dim, false));
test_cases.emplace_back(new test_set(GGML_TYPE_F32, GGML_TYPE_F32, {6, 5, 4, 3}, dim, true));
}

for (int dim = 1; dim < GGML_MAX_DIMS; ++dim) {
test_cases.emplace_back(new test_set(GGML_TYPE_I32, GGML_TYPE_I32, {6, 5, 4, 3}, dim));
test_cases.emplace_back(new test_set(GGML_TYPE_I32, GGML_TYPE_I32, {6, 5, 4, 3}, dim, false));
test_cases.emplace_back(new test_set(GGML_TYPE_I32, GGML_TYPE_I32, {6, 5, 4, 3}, dim, true));
}

// same-type copy
Expand Down
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