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metal : add CONV_2D_DW (depthwise convolution) support (#21565)

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* metal : add CONV_2D_DW (depthwise 2D convolution) support

* test : add perf cases for CONV_2D_DW

* metal : use 3D dispatch for CONV_2D_DW kernel

* metal : add channel-tiled CONV_2D_DW kernel for non-contiguous layouts

* metal : simplify CONV_2D_DW dispatch and trim comments

* metal : merge duplicate CONV_2D_DW pipeline getters

* tests : add F16 CONV2D_DW tests

* cpu : fix F16 kernel support for CONV_2D_DW

* tests : remove commented-out CONV_2D_DW test block

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Sou-ly
2026-07-09 12:29:15 +03:00
committed by GitHub
co-authored by Georgi Gerganov
parent ccb0c34223
commit 92b187c97e
9 changed files with 381 additions and 21 deletions
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ggml/src/ggml-metal/ggml-metal-ops.cpp
+84
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@@ -387,6 +387,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
{
n_fuse = ggml_metal_op_conv_2d(ctx, idx);
} break;
case GGML_OP_CONV_2D_DW:
{
n_fuse = ggml_metal_op_conv_2d_dw(ctx, idx);
} break;
case GGML_OP_CONV_TRANSPOSE_1D:
{
n_fuse = ggml_metal_op_conv_transpose_1d(ctx, idx);
@@ -3742,6 +3746,86 @@ int ggml_metal_op_conv_2d(ggml_metal_op_t ctx, int idx) {
return 1;
}
int ggml_metal_op_conv_2d_dw(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_ASSERT(op->src[1]->type == GGML_TYPE_F32);
GGML_ASSERT(op->type == GGML_TYPE_F32);
GGML_ASSERT(op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32);
const int32_t s0 = ((const int32_t *) op->op_params)[0];
const int32_t s1 = ((const int32_t *) op->op_params)[1];
const int32_t p0 = ((const int32_t *) op->op_params)[2];
const int32_t p1 = ((const int32_t *) op->op_params)[3];
const int32_t d0 = ((const int32_t *) op->op_params)[4];
const int32_t d1 = ((const int32_t *) op->op_params)[5];
ggml_metal_kargs_conv_2d_dw args = {
/*.nb00 =*/ nb00,
/*.nb01 =*/ nb01,
/*.nb02 =*/ nb03,
/*.nb10 =*/ nb10,
/*.nb11 =*/ nb11,
/*.nb12 =*/ nb12,
/*.nb13 =*/ nb13,
/*.nb0 =*/ nb0,
/*.nb1 =*/ nb1,
/*.nb2 =*/ nb2,
/*.nb3 =*/ nb3,
/*.IW =*/ ne10,
/*.IH =*/ ne11,
/*.KW =*/ ne00,
/*.KH =*/ ne01,
/*.C =*/ ne12,
/*.OW =*/ ne0,
/*.OH =*/ ne1,
/*.N =*/ ne13,
/*.s0 =*/ s0,
/*.s1 =*/ s1,
/*.p0 =*/ p0,
/*.p1 =*/ p1,
/*.d0 =*/ d0,
/*.d1 =*/ d1,
};
const bool use_tiled = (nb12 < nb10);
auto pipeline = ggml_metal_library_get_pipeline_conv_2d_dw(lib, op, use_tiled);
int nth = ggml_metal_pipeline_max_theads_per_threadgroup(pipeline);
nth = std::min(nth, 256);
nth = std::max(nth, 1);
const int32_t OW = ne0;
const int32_t OH = ne1;
const int32_t C = ne12;
const int32_t N = ne13;
const int tg_x = use_tiled ? (C + nth - 1) / nth : (OW + nth - 1) / nth;
const int tg_y = OH;
const int tg_z = use_tiled ? OW * N : C * N;
ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[1]), 2);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), 3);
ggml_metal_encoder_dispatch_threadgroups(enc, tg_x, tg_y, tg_z, nth, 1, 1);
return 1;
}
int ggml_metal_op_conv_3d(ggml_metal_op_t ctx, int idx) {
ggml_tensor * op = ctx->node(idx);