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ggml webgpu: minor set rows optimization #16810

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Merged
merged 9 commits into from
Nov 5, 2025
Merged

ggml webgpu: minor set rows optimization #16810

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b566811
Add buffer label and enable dawn-specific toggles to turn off some ch…
reeselevine Oct 15, 2025
f2e187c
Minor set_rows optimization (#4)
neha-ha Oct 27, 2025
2aa05c6
Merge remote-tracking branch 'upstream/master'
reeselevine Oct 27, 2025
51aae63
Comment on dawn toggles
reeselevine Oct 27, 2025
f0cfae4
Remove some comments
reeselevine Oct 27, 2025
da5296e
Merge remote-tracking branch 'upstream/master'
reeselevine Oct 31, 2025
ed710b3
Implement overlap binary operators
reeselevine Nov 1, 2025
b319672
Revert "Implement overlap binary operators"
reeselevine Nov 2, 2025
9a029e4
Disable support for non-contiguous binary_op tensors and leave note f…
reeselevine Nov 2, 2025
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88 changes: 65 additions & 23 deletions ggml/src/ggml-webgpu/ggml-webgpu.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -248,7 +248,7 @@ struct webgpu_context_struct {

webgpu_pipeline memset_pipeline;
webgpu_pipeline mul_mat_pipeline[30][2];
webgpu_pipeline set_rows_pipeline;
webgpu_pipeline set_rows_pipeline[1][2]; // dst->type, vectorized
webgpu_pipeline get_rows_pipeline[30];
webgpu_pipeline get_rows_f32_no_vec_pipeline;
webgpu_pipeline cpy_pipeline[2][2]; // src type, dst type
Expand Down Expand Up @@ -309,10 +309,12 @@ struct ggml_backend_webgpu_context {
struct ggml_backend_webgpu_buffer_context {
webgpu_context webgpu_ctx;
wgpu::Buffer buffer;
std::string label;

ggml_backend_webgpu_buffer_context(webgpu_context ctx, wgpu::Buffer buf) :
ggml_backend_webgpu_buffer_context(webgpu_context ctx, wgpu::Buffer buf, std::string lbl) :
webgpu_ctx(std::move(ctx)),
buffer(std::move(buf)) {}
buffer(std::move(buf)),
label(std::move(lbl)) {}
};

/* End struct definitions */
Expand Down Expand Up @@ -764,10 +766,20 @@ static std::optional<webgpu_command> ggml_webgpu_set_rows(webgpu_context & ctx,
{ .binding = 3, .buffer = error_bufs.dev_buf, .offset = 0, .size = error_bufs.dev_buf.GetSize() }
};

size_t max_wg_size = ctx->max_wg_size_x;
uint32_t wg_x = (src->ne[1] * src->ne[2] * src->ne[3] + max_wg_size - 1) / max_wg_size;
size_t max_wg_size = ctx->max_wg_size_x;

int vectorized = src->ne[0] % 4 == 0;
webgpu_pipeline pipeline = ctx->set_rows_pipeline[0][vectorized];
uint32_t threads;
if (vectorized) {
threads = (src->ne[1] * src->ne[2] * src->ne[3]) * (src->ne[0] / 4);
} else {
threads = src->ne[0] * src->ne[1] * src->ne[2] * src->ne[3];
}

return ggml_backend_webgpu_build(ctx, ctx->set_rows_pipeline, params, entries, wg_x, error_bufs);
uint32_t wg_x = (threads + max_wg_size - 1) / max_wg_size;

return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, error_bufs);
}

static webgpu_command ggml_webgpu_get_rows(webgpu_context & ctx,
Expand Down Expand Up @@ -1336,11 +1348,11 @@ static void ggml_backend_webgpu_buffer_memset_tensor(ggml_backend_buffer_t buffe

WEBGPU_CPU_PROFILE_TOTAL_START(memset_tensor);

WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_memset_tensor(" << buffer << ", " << tensor << ", " << value << ", "
<< offset << ", " << size << ")");

ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context;

WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_memset_tensor(" << buf_ctx->label << ", " << tensor << ", " << value
<< ", " << offset << ", " << size << ")");

size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset;

// This is a trick to set all bytes of a u32 to the same 1 byte value.
Expand All @@ -1354,12 +1366,13 @@ static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer,
const void * data,
size_t offset,
size_t size) {
WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", "
<< offset << ", " << size << ")");
WEBGPU_CPU_PROFILE_TOTAL_START(set_tensor);
ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context;
webgpu_context webgpu_ctx = buf_ctx->webgpu_ctx;

WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_set_tensor(" << buf_ctx->label << ", " << tensor << ", " << data
<< ", " << offset << ", " << size << ")");

size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset;

webgpu_ctx->queue.WriteBuffer(buf_ctx->buffer, total_offset, data, (size / 4) * 4);
Expand Down Expand Up @@ -1397,12 +1410,12 @@ static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer,
void * data,
size_t offset,
size_t size) {
WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", "
<< offset << ", " << size << ")");
WEBGPU_CPU_PROFILE_TOTAL_START(get_tensor);
ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context;
webgpu_context webgpu_ctx = buf_ctx->webgpu_ctx;
wgpu::Device device = webgpu_ctx->device;
ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context;
WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_get_tensor(" << buf_ctx->label << ", " << tensor << ", " << data
<< ", " << offset << ", " << size << ")");
webgpu_context webgpu_ctx = buf_ctx->webgpu_ctx;
wgpu::Device device = webgpu_ctx->device;

size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset;

Expand Down Expand Up @@ -1473,16 +1486,20 @@ static const char * ggml_backend_webgpu_buffer_type_get_name(ggml_backend_buffer

static ggml_backend_buffer_t ggml_backend_webgpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft,
size_t size) {
WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_type_alloc_buffer(" << size << ")");
static std::atomic<int> buffer_count;
int buffer_id = buffer_count++;
std::string buf_name = "tensor_buf" + std::to_string(buffer_id);
WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_type_alloc_buffer_" << buffer_id << ": " << size << " bytes");
ggml_backend_webgpu_device_context * ctx = static_cast<ggml_backend_webgpu_device_context *>(buft->device->context);

wgpu::Buffer buf;
ggml_webgpu_create_buffer(ctx->webgpu_ctx->device, buf,
(size + WEBGPU_STORAGE_BUF_BINDING_MULT - 1) & ~(WEBGPU_STORAGE_BUF_BINDING_MULT - 1),
wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::CopyDst,
"allocated_buffer");
buf_name.c_str());

ggml_backend_webgpu_buffer_context * buf_ctx = new ggml_backend_webgpu_buffer_context(ctx->webgpu_ctx, buf);
ggml_backend_webgpu_buffer_context * buf_ctx =
new ggml_backend_webgpu_buffer_context(ctx->webgpu_ctx, buf, buf_name);

return ggml_backend_buffer_init(buft, ggml_backend_webgpu_buffer_interface, buf_ctx, size);
}
Expand Down Expand Up @@ -1613,8 +1630,10 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) {
}

static void ggml_webgpu_init_set_rows_pipeline(webgpu_context & webgpu_ctx) {
ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->set_rows_pipeline, wgsl_set_rows, "set_rows",
ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x));
ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->set_rows_pipeline[0][0], wgsl_set_rows_f16,
"set_rows_f16", ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x));
ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->set_rows_pipeline[0][1], wgsl_set_rows_f16_vec,
"set_rows_f16_vec", ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x));
}

static void ggml_webgpu_init_get_rows_pipeline(webgpu_context & webgpu_ctx) {
Expand Down Expand Up @@ -1950,8 +1969,10 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
case GGML_OP_SUB:
case GGML_OP_MUL:
case GGML_OP_DIV:
// TODO: support non-contiguous tensors, e.g. for MOE_EXPERT_REDUCE
// see https://github.com/ggml-org/llama.cpp/pull/16857
supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type) &&
(src1->type == op->type);
(src1->type == op->type) && ggml_is_contiguous(src0) && ggml_is_contiguous(src1);
break;
case GGML_OP_CPY:
case GGML_OP_CONT:
Expand Down Expand Up @@ -2129,6 +2150,19 @@ static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t
required_features.push_back(wgpu::FeatureName::TimestampQuery);
#endif

// Enable Dawn-specific toggles to increase native performance
// TODO: Don't enable for WASM builds, they won't have an effect anyways
// TODO: Maybe WebGPU needs a "fast" mode where you can request compilers skip adding checks like these,
// only for native performance?
const char * const deviceEnabledToggles[] = { "skip_validation", "disable_robustness", "disable_workgroup_init",
"disable_polyfills_on_integer_div_and_mod" };
const char * const deviceDisabledToggles[] = { "timestamp_quantization" };
wgpu::DawnTogglesDescriptor deviceTogglesDesc;
deviceTogglesDesc.enabledToggles = deviceEnabledToggles;
deviceTogglesDesc.enabledToggleCount = 4;
deviceTogglesDesc.disabledToggles = deviceDisabledToggles;
deviceTogglesDesc.disabledToggleCount = 1;

wgpu::DeviceDescriptor dev_desc;
dev_desc.requiredLimits = &ctx->limits;
dev_desc.requiredFeatures = required_features.data();
Expand All @@ -2146,6 +2180,7 @@ static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t
GGML_ABORT("ggml_webgpu: Device error! Reason: %d, Message: %s\n", static_cast<int>(reason),
std::string(message).c_str());
});
dev_desc.nextInChain = &deviceTogglesDesc;
ctx->instance.WaitAny(ctx->adapter.RequestDevice(
&dev_desc, wgpu::CallbackMode::AllowSpontaneous,
[ctx](wgpu::RequestDeviceStatus status, wgpu::Device device, wgpu::StringView message) {
Expand Down Expand Up @@ -2243,11 +2278,18 @@ ggml_backend_reg_t ggml_backend_webgpu_reg() {
ctx.name = GGML_WEBGPU_NAME;
ctx.device_count = 1;

const char * const instanceEnabledToggles[] = { "allow_unsafe_apis" };

wgpu::DawnTogglesDescriptor instanceTogglesDesc;
instanceTogglesDesc.enabledToggles = instanceEnabledToggles;
instanceTogglesDesc.enabledToggleCount = 1;
wgpu::InstanceDescriptor instance_descriptor{};
std::vector<wgpu::InstanceFeatureName> instance_features = { wgpu::InstanceFeatureName::TimedWaitAny };
instance_descriptor.requiredFeatures = instance_features.data();
instance_descriptor.requiredFeatureCount = instance_features.size();
webgpu_ctx->instance = wgpu::CreateInstance(&instance_descriptor);
instance_descriptor.nextInChain = &instanceTogglesDesc;

webgpu_ctx->instance = wgpu::CreateInstance(&instance_descriptor);
GGML_ASSERT(webgpu_ctx->instance != nullptr);

static ggml_backend_reg reg = {
Expand Down
45 changes: 38 additions & 7 deletions ...rc/ggml-webgpu/wgsl-shaders/set_rows.wgsl → ...ml-webgpu/wgsl-shaders/set_rows.tmpl.wgsl
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Original file line number Diff line number Diff line change
@@ -1,13 +1,38 @@
#define(VARIANTS)

[
{
"SHADER_SUFFIX": "f16_vec",
"REPLS": {
"TYPE" : "vec4<f32>",
"DST_TYPE": "vec4<f16>",
"VEC_SIZE": 4
}
},
{
"SHADER_SUFFIX": "f16",
"REPLS": {
"TYPE" : "f32",
"DST_TYPE": "f16",
"VEC_SIZE": 1
}
}
]

#end(VARIANTS)

#define(SHADER)

enable f16;

@group(0) @binding(0)
var<storage, read_write> src: array<f32>;
var<storage, read_write> src: array<{{TYPE}}>;

@group(0) @binding(1)
var<storage, read_write> idx: array<u32>;

@group(0) @binding(2)
var<storage, read_write> dst: array<f16>;
var<storage, read_write> dst: array<{{DST_TYPE}}>;

@group(0) @binding(3)
var<storage, read_write> error: atomic<u32>;
Expand Down Expand Up @@ -47,10 +72,14 @@ var<uniform> params: Params;
override wg_size: u32;
@compute @workgroup_size(wg_size)
fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
if (gid.x >= params.n_rows * params.ne2 * params.ne3) {
if (gid.x >= (params.ne3 * params.ne2 * params.n_rows * params.ne0) / {{VEC_SIZE}}) {
return;
}
var i = gid.x;

// getting the row from gid
let elems_per_row = params.ne0 / {{VEC_SIZE}};
var i = gid.x / elems_per_row;

let i_src3 = i / (params.ne2 * params.n_rows);

i = i % (params.ne2 * params.n_rows);
Expand All @@ -75,7 +104,9 @@ fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
let i_dst_row = params.offset_dst + idx_high_val * params.stride_dst1 + i_src2 * params.stride_dst2 + i_src3 * params.stride_dst3;
let i_src_row = params.offset_src + i_src1 * params.stride_src1 + i_src2 * params.stride_src2 + i_src3 * params.stride_src3;

for (var i: u32 = 0; i < params.ne0; i++) {
dst[i_dst_row + i] = f16(src[i_src_row + i]);
}
let col_idx = (gid.x % elems_per_row);
dst[i_dst_row/{{VEC_SIZE}} + col_idx] = {{DST_TYPE}}(src[i_src_row/{{VEC_SIZE}} + col_idx]);
}

#end(SHADER)

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