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llama.cpp
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Use device implicit synchronization

This commit is contained in:
Reese Levine 2025-07-31 12:28:29 -07:00
parent b8012ecc0a
commit cddda7e730
1 changed files with 5 additions and 9 deletions
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14
ggml/src/ggml-webgpu/ggml-webgpu.cpp
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@ -276,8 +276,6 @@ static void ggml_backend_webgpu_build_and_enqueue(webgpu_context &
bool submit_imm = false) { bool submit_imm = false) {
webgpu_param_bufs params_bufs = ctx->param_buf_pool.alloc_bufs(); webgpu_param_bufs params_bufs = ctx->param_buf_pool.alloc_bufs();
std::lock_guard<std::recursive_mutex> lock(ctx->mutex);
ggml_backend_webgpu_map_buffer(ctx, params_bufs.host_buf, wgpu::MapMode::Write, 0, params_bufs.host_buf.GetSize()); 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(); uint32_t * _params = (uint32_t *) params_bufs.host_buf.GetMappedRange();
for (size_t i = 0; i < params.size(); i++) { for (size_t i = 0; i < params.size(); i++) {
@ -318,6 +316,8 @@ static void ggml_backend_webgpu_build_and_enqueue(webgpu_context &
ctx->param_buf_pool.free_bufs({ params_bufs }); ctx->param_buf_pool.free_bufs({ params_bufs });
}); });
} else { } else {
// Lock the context mutex when pushing to the staging vectors.
std::lock_guard<std::recursive_mutex> lock(ctx->mutex);
// Enqueue commands and only submit if we have enough staged commands // Enqueue commands and only submit if we have enough staged commands
ctx->staged_command_bufs.push_back(commands); ctx->staged_command_bufs.push_back(commands);
ctx->staged_param_bufs.push_back(params_bufs); ctx->staged_param_bufs.push_back(params_bufs);
@ -564,7 +564,6 @@ static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer,
size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset; size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset;
std::lock_guard<std::recursive_mutex> lock(webgpu_ctx->mutex);
webgpu_ctx->queue.WriteBuffer(buf_ctx->buffer, total_offset, data, (size / 4) * 4); webgpu_ctx->queue.WriteBuffer(buf_ctx->buffer, total_offset, data, (size / 4) * 4);
if (size % 4 != 0) { if (size % 4 != 0) {
@ -622,11 +621,8 @@ static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer,
encoder.CopyBufferToBuffer(buf_ctx->buffer, total_offset, webgpu_ctx->get_tensor_staging_buf, 0, final_size); encoder.CopyBufferToBuffer(buf_ctx->buffer, total_offset, webgpu_ctx->get_tensor_staging_buf, 0, final_size);
wgpu::CommandBuffer commands = encoder.Finish(); wgpu::CommandBuffer commands = encoder.Finish();
{ // Submit the command buffer to the queue
std::lock_guard<std::recursive_mutex> submit_lock(webgpu_ctx->mutex); webgpu_ctx->queue.Submit(1, &commands);
// Submit the command buffer to the queue
webgpu_ctx->queue.Submit(1, &commands);
}
// Map the staging buffer to read the data // Map the staging buffer to read the data
ggml_backend_webgpu_map_buffer(webgpu_ctx, webgpu_ctx->get_tensor_staging_buf, wgpu::MapMode::Read, 0, final_size); ggml_backend_webgpu_map_buffer(webgpu_ctx, webgpu_ctx->get_tensor_staging_buf, wgpu::MapMode::Read, 0, final_size);
@ -775,7 +771,7 @@ static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, co
std::lock_guard<std::mutex> lock(webgpu_ctx->init_mutex); std::lock_guard<std::mutex> lock(webgpu_ctx->init_mutex);
if (!webgpu_ctx->device_init) { if (!webgpu_ctx->device_init) {
// Initialize device // Initialize device
std::vector<wgpu::FeatureName> required_features = { wgpu::FeatureName::ShaderF16 }; std::vector<wgpu::FeatureName> required_features = { wgpu::FeatureName::ShaderF16, wgpu::FeatureName::ImplicitDeviceSynchronization };
wgpu::DeviceDescriptor dev_desc; wgpu::DeviceDescriptor dev_desc;
dev_desc.requiredLimits = &webgpu_ctx->limits; dev_desc.requiredLimits = &webgpu_ctx->limits;
dev_desc.requiredFeatures = required_features.data(); dev_desc.requiredFeatures = required_features.data();