ggml-webgpu: Add supports for `GGML_OP_REPEAT` (#20230)

* Add GGML_OP_REPEAT to webgpu backend.

* Add i16 support for GGML_OP_REPEAT.
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Masashi Yoshimura 2026-03-12 06:40:36 +09:00 committed by GitHub
parent d28961d81e
commit f2ab047f27
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5 changed files with 198 additions and 25 deletions

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@ -80,7 +80,7 @@ Legend:
| POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
| REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | | ❌ | ❌ |
| REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | | ❌ | ❌ |
| REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| RMS_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
| RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |

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@ -5023,20 +5023,20 @@
"WebGPU: WebGPU","ARGMAX","type=f32,ne=[1024,12,1,1]","support","1","yes","WebGPU"
"WebGPU: WebGPU","ARGMAX","type=f32,ne=[2000,10,1,1]","support","1","yes","WebGPU"
"WebGPU: WebGPU","ARGMAX","type=f32,ne=[5438,3,1,1]","support","1","yes","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,1,1]","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[2,1,1,1]","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,2,1,1]","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,2,1]","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,1,2]","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT","type=i32,ne=[10,5,4,1],nr=[2,1,1,1]","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT","type=i16,ne=[10,5,4,1],nr=[1,1,1,2]","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,1]","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[2,1,1,1]","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,2,1,1]","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,2,1]","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,2]","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT","type=i32,ne=[10,5,4,3],nr=[2,1,1,1]","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT","type=i16,ne=[10,5,4,3],nr=[1,1,1,2]","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,1,1]","support","1","yes","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[2,1,1,1]","support","1","yes","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,2,1,1]","support","1","yes","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,2,1]","support","1","yes","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,1,2]","support","1","yes","WebGPU"
"WebGPU: WebGPU","REPEAT","type=i32,ne=[10,5,4,1],nr=[2,1,1,1]","support","1","yes","WebGPU"
"WebGPU: WebGPU","REPEAT","type=i16,ne=[10,5,4,1],nr=[1,1,1,2]","support","1","yes","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,1]","support","1","yes","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[2,1,1,1]","support","1","yes","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,2,1,1]","support","1","yes","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,2,1]","support","1","yes","WebGPU"
"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,2]","support","1","yes","WebGPU"
"WebGPU: WebGPU","REPEAT","type=i32,ne=[10,5,4,3],nr=[2,1,1,1]","support","1","yes","WebGPU"
"WebGPU: WebGPU","REPEAT","type=i16,ne=[10,5,4,3],nr=[1,1,1,2]","support","1","yes","WebGPU"
"WebGPU: WebGPU","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,1,1],v=0","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[2,1,1,1],v=0","support","0","no","WebGPU"
"WebGPU: WebGPU","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,2,1,1],v=0","support","0","no","WebGPU"

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@ -198,6 +198,22 @@ struct ggml_webgpu_concat_pipeline_key_hash {
}
};
/** Repeat **/
struct ggml_webgpu_repeat_pipeline_key {
int type;
bool operator==(const ggml_webgpu_repeat_pipeline_key & other) const { return type == other.type; }
};
struct ggml_webgpu_repeat_pipeline_key_hash {
size_t operator()(const ggml_webgpu_repeat_pipeline_key & key) const {
size_t seed = 0;
ggml_webgpu_hash_combine(seed, key.type);
return seed;
}
};
/** Binary **/
struct ggml_webgpu_binary_pipeline_key {
@ -431,6 +447,8 @@ class ggml_webgpu_shader_lib {
binary_pipelines; // type/op/inplace/overlap
std::unordered_map<ggml_webgpu_concat_pipeline_key, webgpu_pipeline, ggml_webgpu_concat_pipeline_key_hash>
concat_pipelines; // type
std::unordered_map<ggml_webgpu_repeat_pipeline_key, webgpu_pipeline, ggml_webgpu_repeat_pipeline_key_hash>
repeat_pipelines; // type
std::unordered_map<ggml_webgpu_flash_attn_pipeline_key, webgpu_pipeline, ggml_webgpu_flash_attn_pipeline_key_hash>
flash_attn_pipelines;
std::unordered_map<ggml_webgpu_legacy_mul_mat_pipeline_key,
@ -1147,7 +1165,7 @@ class ggml_webgpu_shader_lib {
}
std::vector<std::string> defines;
std::string variant = "concat";
std::string variant = "concat";
switch (key.type) {
case GGML_TYPE_F32:
@ -1164,15 +1182,56 @@ class ggml_webgpu_shader_lib {
defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
auto processed = preprocessor.preprocess(wgsl_concat, defines);
auto decisions = std::make_shared<ggml_webgpu_generic_shader_decisions>();
decisions->wg_size = context.max_wg_size;
auto processed = preprocessor.preprocess(wgsl_concat, defines);
auto decisions = std::make_shared<ggml_webgpu_generic_shader_decisions>();
decisions->wg_size = context.max_wg_size;
webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
pipeline.context = decisions;
concat_pipelines[key] = pipeline;
pipeline.context = decisions;
concat_pipelines[key] = pipeline;
return concat_pipelines[key];
}
webgpu_pipeline get_repeat_pipeline(const ggml_webgpu_shader_lib_context & context) {
ggml_webgpu_repeat_pipeline_key key = {
.type = context.dst->type,
};
auto it = repeat_pipelines.find(key);
if (it != repeat_pipelines.end()) {
return it->second;
}
std::vector<std::string> defines;
std::string variant = "repeat";
switch (key.type) {
case GGML_TYPE_F32:
defines.push_back("TYPE_F32");
variant += "_f32";
break;
case GGML_TYPE_I32:
defines.push_back("TYPE_I32");
variant += "_i32";
break;
case GGML_TYPE_I16:
defines.push_back("TYPE_I16");
variant += "_i16";
break;
default:
GGML_ABORT("Unsupported type for repeat shader");
}
defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
auto processed = preprocessor.preprocess(wgsl_repeat, defines);
auto decisions = std::make_shared<ggml_webgpu_generic_shader_decisions>();
decisions->wg_size = context.max_wg_size;
webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
pipeline.context = decisions;
repeat_pipelines[key] = pipeline;
return repeat_pipelines[key];
}
webgpu_pipeline get_flash_attn_pipeline(const ggml_webgpu_shader_lib_context & context) {
const bool has_mask = context.src3 != nullptr;
const bool has_sinks = context.src4 != nullptr;

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@ -1567,6 +1567,48 @@ static webgpu_command ggml_webgpu_concat(webgpu_context & ctx,
return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
}
static webgpu_command ggml_webgpu_repeat(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * dst) {
uint32_t ne = (uint32_t) ggml_nelements(dst);
std::vector<uint32_t> params = { ne,
(uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) /
ggml_type_size(src0->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) (src0->ne[0]),
(uint32_t) (src0->ne[1]),
(uint32_t) (src0->ne[2]),
(uint32_t) (src0->ne[3]),
(uint32_t) (dst->ne[0]),
(uint32_t) (dst->ne[1]),
(uint32_t) (dst->ne[2]) };
std::vector<wgpu::BindGroupEntry> entries = {
{ .binding = 0,
.buffer = ggml_webgpu_tensor_buf(src0),
.offset = ggml_webgpu_tensor_align_offset(ctx, src0),
.size = ggml_webgpu_tensor_binding_size(ctx, src0) },
{ .binding = 1,
.buffer = ggml_webgpu_tensor_buf(dst),
.offset = ggml_webgpu_tensor_align_offset(ctx, dst),
.size = ggml_webgpu_tensor_binding_size(ctx, dst) }
};
ggml_webgpu_shader_lib_context shader_lib_ctx = {
.src0 = src0,
.dst = dst,
.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup,
};
webgpu_pipeline pipeline = ctx->shader_lib->get_repeat_pipeline(shader_lib_ctx);
auto * decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size);
return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
}
static webgpu_command ggml_webgpu_rms_norm(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
int inplace = ggml_webgpu_tensor_equal(src, dst);
@ -2158,6 +2200,8 @@ static std::optional<webgpu_command> ggml_webgpu_encode_node(webgpu_context ctx,
return ggml_webgpu_binary_op(ctx, src0, src1, node);
case GGML_OP_CONCAT:
return ggml_webgpu_concat(ctx, src0, src1, node);
case GGML_OP_REPEAT:
return ggml_webgpu_repeat(ctx, src0, node);
case GGML_OP_RMS_NORM:
return ggml_webgpu_rms_norm(ctx, src0, node);
case GGML_OP_ROPE:
@ -2919,10 +2963,10 @@ static ggml_backend_buffer_type_t ggml_backend_webgpu_device_get_buffer_type(ggm
/* .iface = */ {
/* .get_name = */ ggml_backend_webgpu_buffer_type_get_name,
/* .alloc_buffer = */
ggml_backend_webgpu_buffer_type_alloc_buffer, /* .get_alignment = */
ggml_backend_webgpu_buffer_type_get_alignment, /* .get_max_size = */
ggml_backend_webgpu_buffer_type_get_max_size, /* .get_alloc_size = */
ggml_backend_webgpu_buffer_type_get_alloc_size, /* .is_host = */ NULL, // defaults to false
ggml_backend_webgpu_buffer_type_alloc_buffer, /* .get_alignment = */
ggml_backend_webgpu_buffer_type_get_alignment, /* .get_max_size = */
ggml_backend_webgpu_buffer_type_get_max_size, /* .get_alloc_size = */
ggml_backend_webgpu_buffer_type_get_alloc_size, /* .is_host = */ NULL, // defaults to false
},
/* .device = */
dev,
@ -3000,6 +3044,9 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
case GGML_OP_CONCAT:
supports_op = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32);
break;
case GGML_OP_REPEAT:
supports_op = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32 || src0->type == GGML_TYPE_I16);
break;
case GGML_OP_CPY:
case GGML_OP_CONT:
supports_op = ((op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&

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@ -0,0 +1,67 @@
enable f16;
struct Params {
ne: u32,
offset_src0: u32,
offset_dst: u32,
stride_src0_0: u32,
stride_src0_1: u32,
stride_src0_2: u32,
stride_src0_3: u32,
a_ne0: u32,
a_ne1: u32,
a_ne2: u32,
a_ne3: u32,
ne0: u32,
ne1: u32,
ne2: u32,
};
#ifdef TYPE_F32
#define DataType f32
#endif
#ifdef TYPE_I32
#define DataType i32
#endif
#ifdef TYPE_I16
// same size (16-bit) is sufficient for repeat
#define DataType f16
#endif
@group(0) @binding(0)
var<storage, read_write> src0: array<DataType>;
@group(0) @binding(1)
var<storage, read_write> dst: array<DataType>;
@group(0) @binding(2)
var<uniform> params: Params;
@compute @workgroup_size(WG_SIZE)
fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
if (gid.x < params.ne) {
var i = gid.x;
let i3 = i / (params.ne2 * params.ne1 * params.ne0);
i = i % (params.ne2 * params.ne1 * params.ne0);
let i2 = i / (params.ne1 * params.ne0);
i = i % (params.ne1 * params.ne0);
let i1 = i / params.ne0;
let i0 = i % params.ne0;
let a_i0 = i0 % params.a_ne0;
let a_i1 = i1 % params.a_ne1;
let a_i2 = i2 % params.a_ne2;
let a_i3 = i3 % params.a_ne3;
let a_index = a_i0 * params.stride_src0_0 +
a_i1 * params.stride_src0_1 +
a_i2 * params.stride_src0_2 +
a_i3 * params.stride_src0_3;
dst[params.offset_dst + gid.x] = src0[params.offset_src0 + a_index];
}
}