Commit 44be98f05 for llama.cpp
commit 44be98f057e9f9902a8ee12630e181c7f8ec2953
Author: Masashi Yoshimura <yoshimura.masashi.frbs@gmail.com>
Date: Fri Sep 18 20:47:07 2026 +0900
ggml-webgpu: fix supports_op condition for GET_ROWS (#28978)
* fix get_rows vec4 handling
* Add src strides checking to vec4_aligned of get_rows and the new test case.
diff --git a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
index a7ff36030..d1cf78083 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
@@ -106,6 +106,11 @@ struct ggml_webgpu_generic_shader_decisions {
bool inplace = false;
};
+struct ggml_webgpu_get_rows_shader_decisions {
+ uint32_t wg_size = 0;
+ bool vectorized = false;
+};
+
struct ggml_webgpu_binary_shader_decisions {
uint32_t wg_size = 0;
bool inplace = false;
@@ -1551,8 +1556,8 @@ class ggml_webgpu_shader_lib {
return argsort_merge_pipelines[order];
}
- webgpu_pipeline get_get_rows_pipeline(const ggml_webgpu_shader_lib_context & context) {
- const bool vectorized = context.src0->type == GGML_TYPE_F32 && context.dst->ne[0] % 4 == 0;
+ webgpu_pipeline get_get_rows_pipeline(const ggml_webgpu_shader_lib_context & context, bool vec4_aligned) {
+ const bool vectorized = context.src0->type == GGML_TYPE_F32 && context.dst->ne[0] % 4 == 0 && vec4_aligned;
ggml_webgpu_get_rows_pipeline_key key = {};
key.src_type = context.src0->type;
key.vectorized = (int) vectorized;
@@ -1669,8 +1674,9 @@ class ggml_webgpu_shader_lib {
defines.push_back("WG_SIZE=" + std::to_string(context.max_wg_size));
auto processed = preprocessor.preprocess(wgsl_get_rows, defines);
- auto decisions = std::make_shared<ggml_webgpu_generic_shader_decisions>();
+ auto decisions = std::make_shared<ggml_webgpu_get_rows_shader_decisions>();
decisions->wg_size = context.max_wg_size;
+ decisions->vectorized = vectorized;
webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
pipeline.context = decisions;
get_rows_pipelines[key] = pipeline;
diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
index 8b060c41a..9b494d421 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
@@ -1518,15 +1518,24 @@ static webgpu_encoded_op ggml_webgpu_get_rows(webgpu_context & ctx,
shader_lib_ctx.dst = dst;
shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
- webgpu_pipeline pipeline = ctx->shader_lib->get_get_rows_pipeline(shader_lib_ctx);
- auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
+ const uint32_t offset_src = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type));
+ const uint32_t offset_dst = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type));
+ const uint32_t stride_src1 = (uint32_t) (src->nb[1] / ggml_type_size(src->type));
+ const uint32_t stride_src2 = (uint32_t) (src->nb[2] / ggml_type_size(src->type));
+ const uint32_t stride_src3 = (uint32_t) (src->nb[3] / ggml_type_size(src->type));
- std::vector<uint32_t> params = { (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)),
+ const bool vec4_aligned = offset_src % 4 == 0 && offset_dst % 4 == 0 && stride_src1 % 4 == 0 &&
+ stride_src2 % 4 == 0 && stride_src3 % 4 == 0;
+
+ webgpu_pipeline pipeline = ctx->shader_lib->get_get_rows_pipeline(shader_lib_ctx, vec4_aligned);
+ auto * decisions = static_cast<ggml_webgpu_get_rows_shader_decisions *>(pipeline.context.get());
+
+ std::vector<uint32_t> params = { offset_src,
(uint32_t) (ggml_webgpu_tensor_misalignment(ctx, idx) / ggml_type_size(idx->type)),
- (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
- (uint32_t) (src->nb[1] / ggml_type_size(src->type)),
- (uint32_t) (src->nb[2] / ggml_type_size(src->type)),
- (uint32_t) (src->nb[3] / ggml_type_size(src->type)),
+ offset_dst,
+ stride_src1,
+ stride_src2,
+ stride_src3,
(uint32_t) (idx->nb[0] / ggml_type_size(idx->type)),
(uint32_t) (idx->nb[1] / ggml_type_size(idx->type)),
(uint32_t) (idx->nb[2] / ggml_type_size(idx->type)),
@@ -1544,7 +1553,7 @@ static webgpu_encoded_op ggml_webgpu_get_rows(webgpu_context & ctx,
ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, idx),
ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst) };
- uint32_t blocks_per_row = (uint32_t) (dst->ne[0] / (src->type == GGML_TYPE_F32 && dst->ne[0] % 4 == 0 ? 4 : 1));
+ uint32_t blocks_per_row = (uint32_t) (dst->ne[0] / (decisions->vectorized ? 4 : 1));
uint32_t total_rows = (uint32_t) (dst->ne[1] * dst->ne[2] * dst->ne[3]);
uint32_t total_threads = float_parallel ? blocks_per_row * total_rows : total_rows;
uint32_t wg_x = CEIL_DIV(total_threads, decisions->wg_size);
@@ -4333,22 +4342,12 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
src0->type == GGML_TYPE_F32 && (src1->type == GGML_TYPE_I64 || src1->type == GGML_TYPE_I32));
break;
case GGML_OP_GET_ROWS:
- {
- const size_t storage_alignment =
- ctx->webgpu_global_ctx->capabilities.limits.minStorageBufferOffsetAlignment;
- const size_t src_address_unit =
- src0->type == GGML_TYPE_F32 && op->ne[0] % 4 == 0 ? 4 * sizeof(float) : ggml_type_size(src0->type);
- if (ggml_webgpu_tensor_misalignment(src0, storage_alignment) % src_address_unit != 0) {
- break;
- }
- if (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 ||
- ggml_webgpu_supported_qtype(src0->type)) {
- supports_op = (op->type == GGML_TYPE_F32);
- } else if (src0->type == GGML_TYPE_I32) {
- supports_op = op->type == GGML_TYPE_I32;
- }
- break;
+ if (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_webgpu_supported_qtype(src0->type)) {
+ supports_op = (op->type == GGML_TYPE_F32);
+ } else if (src0->type == GGML_TYPE_I32) {
+ supports_op = op->type == GGML_TYPE_I32;
}
+ break;
case GGML_OP_MUL_MAT:
{
switch (src1->type) {
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 260ffef66..30792e409 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -2338,24 +2338,26 @@ struct test_get_rows : public test_case {
const int be2; // batch size
const bool v; // view src1
const bool vs0; // view src0
+ const int offset_cols; // // column offset of the view src0
std::string vars() override {
- return VARS_TO_STR8(type, n, m, r, be1, be2, v, vs0);
+ return VARS_TO_STR9(type, n, m, r, be1, be2, v, vs0, offset_cols);
}
- test_get_rows(ggml_type type = GGML_TYPE_F32, int n = 10, int m = 5, int r = 3, int be1 = 1, int be2 = 1, bool v = false, bool vs0 = false)
- : type(type), n(n), m(m), r(r), be1(be1), be2(be2), v(v), vs0(vs0) {}
+ test_get_rows(ggml_type type = GGML_TYPE_F32, int n = 10, int m = 5, int r = 3, int be1 = 1, int be2 = 1, bool v = false, bool vs0 = false, int offset_cols = 0)
+ : type(type), n(n), m(m), r(r), be1(be1), be2(be2), v(v), vs0(vs0), offset_cols(offset_cols) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * in;
if (vs0) {
const int offset_rows = 3;
const int padded_m = m + offset_rows;
- ggml_tensor * in_padded = ggml_new_tensor_4d(ctx, type, n, padded_m, be1, be2);
+ const int padded_n = n + offset_cols;
+ ggml_tensor * in_padded = ggml_new_tensor_4d(ctx, type, padded_n, padded_m, be1, be2);
ggml_set_name(in_padded, "in_padded");
in = ggml_view_4d(ctx, in_padded, n, m, be1, be2,
in_padded->nb[1], in_padded->nb[2], in_padded->nb[3],
- offset_rows * in_padded->nb[1]);
+ offset_cols * in_padded->nb[0] + offset_rows * in_padded->nb[1]);
ggml_set_name(in, "in_view");
} else {
in = ggml_new_tensor_4d(ctx, type, n, m, be1, be2);
@@ -9043,6 +9045,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
}
}
}
+ test_cases.emplace_back(new test_get_rows(GGML_TYPE_F32, 256, 8, 2, 1, 1, false, true, 3));
test_cases.emplace_back(new test_get_rows_back(GGML_TYPE_F32, 1, 8, 2, 1, false));
test_cases.emplace_back(new test_get_rows_back(GGML_TYPE_F32, 1, 70000, 4, 1, false)); // row count > CUDA grid-y limit (65535)