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ggml-webgpu(shader): support conv2d kernels. (#21964)

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* ggml(webgpu): fix the busy-polls in Emscripten  in the waitAny after #20618, and remove the busy webgpu log

* Merge with upstream

* Fix GET_ROWS packed integer NaN when using f16 as memory buffer in shader quants

* Update Unary wgsl EXP and EXPM1 for f16 stability

* Fix GET_ROWS IQ4_XS strcut for NaN f16 canonicalization

* Fix numerical percision for unary sqrt when working with f16

* Fix NaN canonicalization for packed integers using f16

* Update err threshold for binary div ops when using f16

* backend: Keep one Dawn/WebGPU instance alive for the lifetime of the static backend

* clean: uncomment existing code logs

* clean: clean the unncessary debug info

* Refactor and generalize dequant helpers

* Remove deprecated quant structs

* Refactor shader defines to reduce repetition

* Remove error override for F16 type

* fix: fix the accidential removal of the proper initialization of ctx

* clean: clean legacy and format code

* fix: did not modify tests ops

* shader(conv2d): add conv2d shader kernels and pass f32 and f16 tests

* shader(conv2d): fix the out of bounds memory access in the weight indexing

* shader(conv2d): clean unused variables and optimize the computation

* merge: use the new entries function

* clean: address the formatting issues

* clean: address the warning issues

* clear: clean the shader editorconfig-checker issues

* clear: clean the shader editorconfig-checker with utf-8

---------

Co-authored-by: Jeremy J. Hartmann <jeremy@mtion.tv>
This commit is contained in:
Chen Yuan
2026-04-21 23:18:57 -04:00
committed by GitHub
parent 0dedb9ef7a
commit ca7f7b7b94
3 changed files with 317 additions and 0 deletions
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ggml/src/ggml-webgpu/ggml-webgpu.cpp
+89
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@@ -8,6 +8,7 @@
#include "ggml-backend-impl.h"
#include "ggml-impl.h"
#include "ggml-webgpu-shader-lib.hpp"
#include "ggml.h"
#ifdef __EMSCRIPTEN__
# include <emscripten/emscripten.h>
@@ -921,6 +922,87 @@ static webgpu_encoded_op ggml_webgpu_solve_tri(webgpu_context & ctx,
return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
}
static webgpu_encoded_op ggml_webgpu_conv_2d(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);
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) (src0->nb[0] / ggml_type_size(src0->type)),
(uint32_t) (src0->nb[1] / ggml_type_size(src0->type)),
(uint32_t) (src0->nb[2] / ggml_type_size(src0->type)),
(uint32_t) (src0->nb[3] / ggml_type_size(src0->type)),
(uint32_t) (src1->nb[0] / ggml_type_size(src1->type)),
(uint32_t) (src1->nb[1] / ggml_type_size(src1->type)),
(uint32_t) (src1->nb[2] / ggml_type_size(src1->type)),
(uint32_t) (src1->nb[3] / ggml_type_size(src1->type)),
(uint32_t) (dst->nb[0] / ggml_type_size(dst->type)),
(uint32_t) (dst->nb[1] / ggml_type_size(dst->type)),
(uint32_t) (dst->nb[2] / ggml_type_size(dst->type)),
(uint32_t) (dst->nb[3] / ggml_type_size(dst->type)),
(uint32_t) src0->ne[0],
(uint32_t) src0->ne[1],
(uint32_t) src0->ne[2],
(uint32_t) src1->ne[0],
(uint32_t) src1->ne[1],
(uint32_t) dst->ne[0],
(uint32_t) dst->ne[1],
(uint32_t) dst->ne[2],
(uint32_t) dst->ne[3],
(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),
};
uint32_t max_wg_size =
std::min((uint32_t) WEBGPU_MAX_WG_SIZE, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupSizeX);
uint32_t wg_size =
std::min((uint32_t) ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup, max_wg_size);
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 = wg_size;
webgpu_pipeline pipeline = ctx->shader_lib->get_conv2d_pipeline(shader_lib_ctx);
auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
uint32_t n_out = ggml_nelements(dst);
uint32_t total_wg = CEIL_DIV(n_out, decisions->wg_size);
uint32_t max_wg = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension;
uint32_t wg_x = std::min(total_wg, max_wg);
uint32_t wg_y = CEIL_DIV(total_wg, wg_x);
return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
}
static webgpu_encoded_op ggml_webgpu_ssm_conv(webgpu_context & ctx,
ggml_tensor * src0,
ggml_tensor * src1,
@@ -2477,6 +2559,8 @@ static std::optional<webgpu_encoded_op> ggml_webgpu_encode_node(webgpu_context c
case GGML_OP_SUM:
case GGML_OP_SUM_ROWS:
return ggml_webgpu_sum_rows(ctx, src0, node);
case GGML_OP_CONV_2D:
return ggml_webgpu_conv_2d(ctx, src0, src1, node);
default:
return std::nullopt;
}
@@ -3495,6 +3579,11 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
case GGML_OP_SOLVE_TRI:
supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32;
break;
case GGML_OP_CONV_2D:
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);
break;
case GGML_OP_SSM_CONV:
supports_op = op->type == GGML_TYPE_F32;
break;