Lumpiasty/llama.cpp
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

webgpu : add CONV_2D_DW (depthwise conv2d) kernel (#25847)

Browse Source 
* webgpu : add CONV_2D_DW (depthwise conv2d) kernel

Implement GGML_OP_CONV_2D_DW for the WebGPU backend,
ported from the Vulkan backend's conv2d_dw.comp.

Assisted-by: Claude Opus-4.8

* Remove unnecessary comments in webgpu support

* update supported ops tables, triggered by adding webgpu CONV_2D_DW
This commit is contained in:
m1el
2026-07-22 17:24:44 +09:00
committed by GitHub
parent c5a4a0bb83
commit 7347430f44
5 changed files with 2915 additions and 958 deletions
Download Patch File Download Diff File Expand all files Collapse all files
ggml/src/ggml-webgpu/ggml-webgpu.cpp
+69
View File
@@ -978,6 +978,67 @@ static webgpu_encoded_op ggml_webgpu_conv_2d(webgpu_context & ctx,
return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
}
// Same param/binding layout as conv_2d; the shader differs
static webgpu_encoded_op ggml_webgpu_conv_2d_dw(webgpu_context & ctx,
ggml_tensor * src0,
ggml_tensor * src1,
ggml_tensor * dst) {
const int32_t s0 = ggml_get_op_params_i32(dst, 0);
const int32_t s1 = ggml_get_op_params_i32(dst, 1);
const int32_t p0 = ggml_get_op_params_i32(dst, 2);
const int32_t p1 = ggml_get_op_params_i32(dst, 3);
const int32_t d0 = ggml_get_op_params_i32(dst, 4);
const int32_t d1 = ggml_get_op_params_i32(dst, 5);
// Scalar params matching conv2d_dw.wgsl (weight src0 [KW,KH,1,C], input src1, output dst).
std::vector<uint32_t> params = {
(uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
(uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
(uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
(uint32_t) ggml_nelements(dst),
(uint32_t) dst->ne[2],
(uint32_t) dst->ne[3],
(uint32_t) dst->ne[0],
(uint32_t) dst->ne[1],
(uint32_t) src1->ne[0],
(uint32_t) src1->ne[1],
(uint32_t) src0->ne[0],
(uint32_t) src0->ne[1],
(uint32_t) s0,
(uint32_t) s1,
(uint32_t) p0,
(uint32_t) p1,
(uint32_t) d0,
(uint32_t) d1,
};
std::vector<wgpu::BindGroupEntry> entries = {
ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0),
ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1),
ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst),
};
ggml_webgpu_shader_lib_context shader_lib_ctx = {};
shader_lib_ctx.src0 = src0;
shader_lib_ctx.src1 = src1;
shader_lib_ctx.dst = dst;
shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
// Input layout: contiguous -> WHCN, contiguous-channels -> CWHN
const bool whcn = ggml_is_contiguous(src1);
webgpu_pipeline pipeline = ctx->shader_lib->get_conv2d_dw_pipeline(shader_lib_ctx, whcn);
auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
uint32_t wg_x;
uint32_t wg_y;
uint32_t total_wg = CEIL_DIV((uint32_t) ggml_nelements(dst), decisions->wg_size);
compute_2d_workgroups(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, wg_x, wg_y);
return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
}
static webgpu_encoded_op ggml_webgpu_im2col(webgpu_context & ctx,
ggml_tensor * src0,
ggml_tensor * src1,
@@ -3164,6 +3225,8 @@ static std::optional<webgpu_encoded_op> ggml_webgpu_encode(webgpu_context ctx,
return ggml_webgpu_sum_rows(ctx, src0, node);
case GGML_OP_CONV_2D:
return ggml_webgpu_conv_2d(ctx, src0, src1, node);
case GGML_OP_CONV_2D_DW:
return ggml_webgpu_conv_2d_dw(ctx, src0, src1, node);
case GGML_OP_IM2COL:
return ggml_webgpu_im2col(ctx, src0, src1, node);
case GGML_OP_UPSCALE:
@@ -4349,6 +4412,12 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) &&
(src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);
break;
case GGML_OP_CONV_2D_DW:
supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) &&
(src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16) &&
(ggml_is_contiguous(src1) || ggml_is_contiguous_channels(src1));
break;
case GGML_OP_IM2COL:
supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);