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ggml: Add basic SET_ROWS support in WebGPU (#15137)

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* Begin work on set_rows

* Work on set rows

* Add error buffers for reporting unsupported SET_ROWS indices

* Remove extra comments
This commit is contained in:
Reese Levine
2025-08-06 15:14:40 -07:00
committed by GitHub
parent 756cfea826
commit 5fd160bbd9
3 changed files with 286 additions and 35 deletions
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2
.github/workflows/build.yml vendored
View File

@@ -179,7 +179,6 @@ jobs:
- name: Test
id: cmake_test
run: |
export LLAMA_SET_ROWS=0
cd build
ctest -L main --verbose --timeout 900
@@ -438,7 +437,6 @@ jobs:
- name: Test
id: cmake_test
run: |
export LLAMA_SET_ROWS=0
cd build
# This is using llvmpipe and runs slower than other backends
ctest -L main --verbose --timeout 3600

237
ggml/src/ggml-webgpu/ggml-webgpu.cpp
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@@ -19,18 +19,21 @@
#include <vector>
#ifdef GGML_WEBGPU_DEBUG
# define WEBGPU_LOG_DEBUG(msg) std::cout << msg << std::endl
# define WEBGPU_LOG_DEBUG(msg) std::cout << msg << std::endl
# define WEBGPU_DEBUG_BUF_ELEMS 32
#else
# define WEBGPU_LOG_DEBUG(msg) ((void) 0)
#endif // GGML_WEBGPU_DEBUG
/* Constants */
#define WEBGPU_COMMAND_SUBMIT_BATCH_SIZE 16
#define WEBGPU_MUL_MAT_WG_SIZE 64
#define WEBGPU_NUM_PARAM_BUFS 100
#define WEBGPU_PARAMS_BUF_SIZE_BYTES 256
#define WEBGPU_STORAGE_BUF_BINDING_MULT 4 // a storage buffer binding size must be a multiple of 4
#define WEBGPU_COMMAND_SUBMIT_BATCH_SIZE 16
#define WEBGPU_MUL_MAT_WG_SIZE 64
#define WEBGPU_NUM_PARAM_BUFS 100
#define WEBGPU_PARAMS_BUF_SIZE_BYTES 128 // enough for 32 parameters
#define WEBGPU_NUM_SET_ROWS_ERROR_BUFS 32
#define WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES 4
#define WEBGPU_STORAGE_BUF_BINDING_MULT 4 // a storage buffer binding size must be a multiple of 4
/* End Constants */
@@ -54,46 +57,42 @@ static void ggml_webgpu_create_buffer(wgpu::Device & device,
wgpu::BufferUsage usage,
const char * label);
struct webgpu_param_bufs {
struct webgpu_pool_bufs {
wgpu::Buffer host_buf;
wgpu::Buffer dev_buf;
};
// Holds a pool of parameter buffers for WebGPU operations
struct webgpu_param_buf_pool {
std::vector<webgpu_param_bufs> free;
struct webgpu_buf_pool {
std::vector<webgpu_pool_bufs> free;
std::mutex mutex;
std::condition_variable cv;
void init(wgpu::Device device) {
for (int i = 0; i < WEBGPU_NUM_PARAM_BUFS; i++) {
void init(wgpu::Device device,
int num_bufs,
size_t buf_size,
wgpu::BufferUsage dev_buf_usage,
wgpu::BufferUsage host_buf_usage) {
for (int i = 0; i < num_bufs; i++) {
wgpu::Buffer host_buf;
wgpu::Buffer dev_buf;
ggml_webgpu_create_buffer(device,
host_buf,
WEBGPU_PARAMS_BUF_SIZE_BYTES,
wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite,
"ggml_webgpu_host_params_buf");
ggml_webgpu_create_buffer(device,
dev_buf,
WEBGPU_PARAMS_BUF_SIZE_BYTES,
wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform,
"ggml_webgpu_dev_params_buf");
ggml_webgpu_create_buffer(device, host_buf, buf_size, host_buf_usage, "ggml_webgpu_host_pool_buf");
ggml_webgpu_create_buffer(device, dev_buf, buf_size, dev_buf_usage, "ggml_webgpu_dev_pool_buf");
free.push_back({ host_buf, dev_buf });
}
}
webgpu_param_bufs alloc_bufs() {
webgpu_pool_bufs alloc_bufs() {
std::unique_lock<std::mutex> lock(mutex);
cv.wait(lock, [this] { return !free.empty(); });
webgpu_param_bufs bufs = free.back();
webgpu_pool_bufs bufs = free.back();
free.pop_back();
return bufs;
}
void free_bufs(std::vector<webgpu_param_bufs> bufs) {
void free_bufs(std::vector<webgpu_pool_bufs> bufs) {
std::lock_guard<std::mutex> lock(mutex);
free.insert(free.end(), bufs.begin(), bufs.end());
cv.notify_all();
@@ -121,10 +120,12 @@ struct webgpu_context_struct {
bool device_init = false;
webgpu_param_buf_pool param_buf_pool;
webgpu_buf_pool param_buf_pool;
webgpu_buf_pool set_rows_error_buf_pool;
wgpu::ComputePipeline memset_pipeline;
wgpu::ComputePipeline mul_mat_pipeline;
wgpu::ComputePipeline set_rows_pipeline;
wgpu::ComputePipeline cpy_pipeline;
size_t memset_bytes_per_thread;
@@ -136,9 +137,16 @@ struct webgpu_context_struct {
std::vector<wgpu::CommandBuffer> staged_command_bufs;
// Parameter buffers associated with the staged command buffers
std::vector<webgpu_param_bufs> staged_param_bufs;
std::vector<webgpu_pool_bufs> staged_param_bufs;
// Buffers associated with set_rows operations, used to store potential errors
std::vector<webgpu_pool_bufs> staged_set_row_error_bufs;
std::vector<wgpu::FutureWaitInfo> callback_futures;
#ifdef GGML_WEBGPU_DEBUG
wgpu::Buffer debug_host_buf;
wgpu::Buffer debug_dev_buf;
#endif
};
typedef std::shared_ptr<webgpu_context_struct> webgpu_context;
@@ -249,20 +257,55 @@ static void ggml_backend_webgpu_submit_queue(webgpu_context & ctx) {
return;
}
ctx->queue.Submit(ctx->staged_command_bufs.size(), ctx->staged_command_bufs.data());
// If there are SET_ROWS operations in this submission, copy their error buffers to the host.
if (ctx->staged_set_row_error_bufs.size() > 0) {
wgpu::CommandEncoder encoder = ctx->device.CreateCommandEncoder();
for (auto & error_bufs : ctx->staged_set_row_error_bufs) {
// Copy the error buffer to the host buffer
encoder.CopyBufferToBuffer(error_bufs.dev_buf, 0, error_bufs.host_buf, 0, error_bufs.host_buf.GetSize());
}
wgpu::CommandBuffer commands = encoder.Finish();
ctx->queue.Submit(1, &commands);
}
ctx->staged_command_bufs.clear();
std::vector<webgpu_param_bufs> staged_param_bufs = std::move(ctx->staged_param_bufs);
std::vector<webgpu_pool_bufs> staged_param_bufs = std::move(ctx->staged_param_bufs);
std::vector<webgpu_pool_bufs> staged_set_row_error_bufs = std::move(ctx->staged_set_row_error_bufs);
// Free the staged parameter buffers once the submission completes
wgpu::Future f = ctx->queue.OnSubmittedWorkDone(
wgpu::Future p_f = ctx->queue.OnSubmittedWorkDone(
wgpu::CallbackMode::AllowSpontaneous,
[ctx, staged_param_bufs](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) {
if (status != wgpu::QueueWorkDoneStatus::Success) {
GGML_LOG_ERROR("ggml_webgpu: Failed to submit commands: %s\n", message.data);
}
// Free the staged parameter buffers
// Free the staged buffers
ctx->param_buf_pool.free_bufs(staged_param_bufs);
});
ctx->callback_futures.push_back({ f });
ctx->callback_futures.push_back({ p_f });
// Check for errrors in SET_ROWS operations
for (auto & error_bufs : staged_set_row_error_bufs) {
wgpu::Future f = error_bufs.host_buf.MapAsync(
wgpu::MapMode::Read,
0,
error_bufs.host_buf.GetSize(),
wgpu::CallbackMode::AllowSpontaneous,
[ctx, error_bufs](wgpu::MapAsyncStatus status, wgpu::StringView message) {
if (status != wgpu::MapAsyncStatus::Success) {
GGML_LOG_ERROR("ggml_webgpu: Failed to map error buffer: %s\n", message.data);
} else {
const uint32_t * error_data = (const uint32_t *) error_bufs.host_buf.GetConstMappedRange();
if (*error_data) {
GGML_ABORT("ggml_webgpu: SET_ROWS index > 2^32, unsupported.");
}
// We can't unmap in here due to WebGPU reentrancy limitations.
ctx->set_rows_error_buf_pool.free_bufs({ error_bufs });
}
});
ctx->callback_futures.push_back({ f });
}
}
static void ggml_backend_webgpu_map_buffer(webgpu_context & ctx,
@@ -283,13 +326,34 @@ static void ggml_backend_webgpu_map_buffer(webgpu_context & ctx,
UINT64_MAX);
}
#ifdef GGML_WEBGPU_DEBUG
// This function adds debugging information to shaders, as WebGPU does not support printing directly.
// To use, add a bind group entry to the setup for the shader you are debugging, add the buffer and
// debug statements in the shader, and then call this function after encoding the commands and submitting them.
static void ggml_backend_webgpu_debug(webgpu_context & ctx) {
wgpu::CommandEncoder encoder = ctx->device.CreateCommandEncoder();
encoder.CopyBufferToBuffer(ctx->debug_dev_buf, 0, ctx->debug_host_buf, 0, ctx->debug_host_buf.GetSize());
wgpu::CommandBuffer commands = encoder.Finish();
ctx->queue.Submit(1, &commands);
ggml_backend_webgpu_map_buffer(ctx, ctx->debug_host_buf, wgpu::MapMode::Read, 0, ctx->debug_host_buf.GetSize());
const uint32_t * debug_data = (const uint32_t *) ctx->debug_host_buf.GetConstMappedRange();
std::cout << "debug data:";
for (size_t i = 0; i < WEBGPU_DEBUG_BUF_ELEMS; i++) {
std::cout << " " << i << ": " << debug_data[i];
}
std::cout << "\n";
ctx->debug_host_buf.Unmap();
}
#endif
static void ggml_backend_webgpu_build_and_enqueue(webgpu_context & ctx,
wgpu::ComputePipeline & pipeline,
std::vector<uint32_t> params,
std::vector<wgpu::BindGroupEntry> bind_group_entries,
uint32_t wg_x,
bool submit_and_wait = false) {
webgpu_param_bufs params_bufs = ctx->param_buf_pool.alloc_bufs();
webgpu_pool_bufs params_bufs = ctx->param_buf_pool.alloc_bufs();
ggml_backend_webgpu_map_buffer(ctx, params_bufs.host_buf, wgpu::MapMode::Write, 0, params_bufs.host_buf.GetSize());
uint32_t * _params = (uint32_t *) params_bufs.host_buf.GetMappedRange();
@@ -429,6 +493,76 @@ static void ggml_webgpu_cpy(webgpu_context & ctx, ggml_tensor * src, ggml_tensor
ggml_backend_webgpu_build_and_enqueue(ctx, ctx->cpy_pipeline, params, entries, wg_x);
}
static void ggml_webgpu_set_rows(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * idx, ggml_tensor * dst) {
// For set rows specifically, we need to check if src and idx are empty tensors.
if (ggml_is_empty(src) || ggml_is_empty(idx)) {
return;
}
webgpu_pool_bufs error_bufs = ctx->set_rows_error_buf_pool.alloc_bufs();
if (error_bufs.host_buf.GetMapState() == wgpu::BufferMapState::Mapped) {
error_bufs.host_buf.Unmap();
}
size_t src_offset = ggml_backend_webgpu_tensor_offset(src);
// assumes power of 2 offset alignment
size_t src_misalignment = src_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
// align to minimum offset alignment
src_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
size_t idx_offset = ggml_backend_webgpu_tensor_offset(idx);
size_t idx_misalignment = idx_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
idx_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
size_t dst_offset = ggml_backend_webgpu_tensor_offset(dst);
size_t dst_misalignment = dst_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
dst_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
std::vector<uint32_t> params = { (uint32_t) (src_misalignment / ggml_type_size(src->type)),
(uint32_t) (idx_misalignment / ggml_type_size(idx->type)),
(uint32_t) (dst_misalignment / ggml_type_size(dst->type)),
// Convert byte-strides to element-strides
(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)),
(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)),
(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)),
// Shape of src
(uint32_t) src->ne[0],
(uint32_t) src->ne[1],
(uint32_t) src->ne[2],
(uint32_t) src->ne[3],
// Shape of idx
(uint32_t) (idx->ne[1]),
(uint32_t) (idx->ne[2]) };
std::vector<wgpu::BindGroupEntry> entries = {
{ .binding = 0,
.buffer = ggml_backend_webgpu_tensor_buf(src),
.offset = ggml_backend_webgpu_tensor_offset(src),
.size = ggml_nbytes(src) },
{ .binding = 1,
.buffer = ggml_backend_webgpu_tensor_buf(idx),
.offset = ggml_backend_webgpu_tensor_offset(idx),
.size = ggml_nbytes(idx) },
{ .binding = 2,
.buffer = ggml_backend_webgpu_tensor_buf(dst),
.offset = ggml_backend_webgpu_tensor_offset(dst),
.size = ggml_nbytes(dst) },
{ .binding = 3, .buffer = error_bufs.dev_buf, .offset = 0, .size = error_bufs.dev_buf.GetSize() }
};
size_t max_wg_size = ctx->limits.maxComputeWorkgroupSizeX;
uint32_t wg_x = (src->ne[1] * src->ne[2] * src->ne[3] + max_wg_size - 1) / max_wg_size;
std::lock_guard<std::recursive_mutex> lock(ctx->mutex);
ctx->staged_set_row_error_bufs.push_back(error_bufs);
ggml_backend_webgpu_build_and_enqueue(ctx, ctx->set_rows_pipeline, params, entries, wg_x);
}
static void ggml_webgpu_mul_mat(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst) {
std::vector<uint32_t> params = {
(uint32_t) dst->ne[1], // number of rows in result (M)
@@ -487,6 +621,11 @@ static bool ggml_webgpu_encode_node(webgpu_context ctx, ggml_tensor * node) {
ggml_webgpu_cpy(ctx, src0, node);
break;
}
case GGML_OP_SET_ROWS:
{
ggml_webgpu_set_rows(ctx, src0, src1, node);
break;
}
case GGML_OP_MUL_MAT:
{
ggml_webgpu_mul_mat(ctx, src0, src1, node);
@@ -771,6 +910,14 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) {
ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline, wgsl_mul_mat, "mul_mat");
}
static void ggml_webgpu_init_set_rows_pipeline(webgpu_context & webgpu_ctx) {
std::vector<wgpu::ConstantEntry> constants(1);
constants[0].key = "wg_size";
constants[0].value = webgpu_ctx->limits.maxComputeWorkgroupSizeX;
ggml_webgpu_create_pipeline(
webgpu_ctx->device, webgpu_ctx->set_rows_pipeline, wgsl_set_rows, "set_rows", constants);
}
static void ggml_webgpu_init_cpy_pipeline(webgpu_context & webgpu_ctx) {
std::vector<wgpu::ConstantEntry> constants(1);
constants[0].key = "wg_size";
@@ -827,11 +974,35 @@ static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, co
webgpu_ctx->queue = webgpu_ctx->device.GetQueue();
// Create buffer pool for shader parameters
webgpu_ctx->param_buf_pool.init(webgpu_ctx->device);
webgpu_ctx->param_buf_pool.init(webgpu_ctx->device,
WEBGPU_NUM_PARAM_BUFS,
WEBGPU_PARAMS_BUF_SIZE_BYTES,
wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform,
wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite);
webgpu_ctx->set_rows_error_buf_pool.init(webgpu_ctx->device,
WEBGPU_NUM_SET_ROWS_ERROR_BUFS,
WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES,
wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::Storage,
wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead);
ggml_webgpu_init_memset_pipeline(webgpu_ctx);
ggml_webgpu_init_mul_mat_pipeline(webgpu_ctx);
ggml_webgpu_init_set_rows_pipeline(webgpu_ctx);
ggml_webgpu_init_cpy_pipeline(webgpu_ctx);
#ifdef GGML_WEBGPU_DEBUG
// Initialize debug buffers
ggml_webgpu_create_buffer(webgpu_ctx->device,
webgpu_ctx->debug_host_buf,
WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t),
wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead,
"debug_host_buf");
ggml_webgpu_create_buffer(webgpu_ctx->device,
webgpu_ctx->debug_dev_buf,
WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t),
wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc,
"debug_dev_buf");
#endif
webgpu_ctx->device_init = true;
}
@@ -882,7 +1053,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
return true;
case GGML_OP_CPY:
case GGML_OP_CPY | GGML_OP_SET_ROWS:
return op->type == GGML_TYPE_F16 && op->src[0]->type == GGML_TYPE_F32;
case GGML_OP_MUL_MAT:
return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32;

82
ggml/src/ggml-webgpu/wgsl-shaders/set_rows.wgsl Normal file
View File

@@ -0,0 +1,82 @@
enable f16;
@group(0) @binding(0)
var<storage, read_write> src: array<f32>;
@group(0) @binding(1)
var<storage, read_write> idx: array<u32>;
@group(0) @binding(2)
var<storage, read_write> dst: array<f16>;
@group(0) @binding(3)
var<storage, read_write> error: atomic<u32>;
struct Params {
offset_src: u32, // in elements
offset_idx: u32, // in elements
offset_dst: u32, // in elements
// Strides (in elements)
stride_src1: u32,
stride_src2: u32,
stride_src3: u32,
stride_idx0: u32,
stride_idx1: u32,
stride_idx2: u32,
stride_dst1: u32,
stride_dst2: u32,
stride_dst3: u32,
// Shape of src
ne0: u32,
n_rows: u32,
ne2: u32,
ne3: u32,
// Shape of idx
idx1: u32,
idx2: u32,
};
@group(0) @binding(4)
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) {
return;
}
var i = gid.x;
let i_src3 = i / (params.ne2 * params.n_rows);
let i_dst3 = i / (params.ne2 * 3);
i = i % (params.ne2 * params.n_rows);
let i_src2 = i / params.n_rows;
let i_src1 = i % params.n_rows;
let i_idx2 = i_src3 % params.idx2;
let i_idx1 = i_src2 % params.idx1;
let i_idx0 = i_src1;
let idx_high = (params.offset_idx + i_idx0 * params.stride_idx0 + i_idx1 * params.stride_idx1 + i_idx2 * params.stride_idx2) * 2;
let idx_high_val = idx[idx_high];
let idx_low_val = idx[idx_high + 1];
if (idx_low_val != 0) {
// Upper bits of index are not zero, output will be incorrect
atomicStore(&error, 1);
return;
}
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]);
}
}