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e83905696e
* Show install instructions when dawn library is missing * Handle missing dawn in ops_webgpu * Simplify * Solve f-string backlash error
244 lines
13 KiB
Python
244 lines
13 KiB
Python
import functools, struct
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from tinygrad.device import Compiled, Allocator, Compiler
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from tinygrad.renderer.wgsl import WGSLRenderer
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from tinygrad.helpers import round_up, OSX
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from tinygrad.runtime.autogen import webgpu
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from typing import List, Any
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import ctypes
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try:
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instance = webgpu.wgpuCreateInstance(webgpu.WGPUInstanceDescriptor(features = webgpu.WGPUInstanceFeatures(timedWaitAnyEnable = True)))
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except AttributeError:
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raise RuntimeError("Cannot find dawn library. Install it with: " + ("brew tap wpmed92/dawn && brew install dawn" if OSX else
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"sudo curl -L https://github.com/wpmed92/pydawn/releases/download/v0.1.6/libwebgpu_dawn.so -o /usr/lib/libwebgpu_dawn.so"))
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def to_c_string(_str): return ctypes.create_string_buffer(_str.encode('utf-8'))
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def from_wgpu_str(string_view): return ctypes.string_at(string_view.data, string_view.length).decode("utf-8")
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def to_wgpu_str(_str):
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return webgpu.WGPUStringView(data=ctypes.cast(ctypes.pointer(to_c_string(_str)), ctypes.POINTER(ctypes.c_char)), length=len(_str))
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def wgpu_wait(future):
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assert webgpu.wgpuInstanceWaitAny(instance, 1, webgpu.WGPUFutureWaitInfo(future=future), 2**64-1) == webgpu.WGPUWaitStatus_Success, "Future failed"
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def create_cb_info(cb_info_type, cb_type, cb): return cb_info_type(nextInChain=None, mode=webgpu.WGPUCallbackMode_WaitAnyOnly, callback=cb_type(cb))
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def write_buffer(device, buf, offset, src):
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src = bytearray(src)
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webgpu.wgpuQueueWriteBuffer(webgpu.wgpuDeviceGetQueue(device), buf, offset, (ctypes.c_uint8 * len(src)).from_buffer(src), len(src))
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def map_buffer(buf, size):
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result: List[Any] = []
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def cb(status, msg, u1, u2): result[:] = status, from_wgpu_str(msg)
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cb_info = create_cb_info(webgpu.WGPUBufferMapCallbackInfo2, webgpu.WGPUBufferMapCallback2, cb)
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wgpu_wait(webgpu.wgpuBufferMapAsync2(buf, webgpu.WGPUMapMode_Read, 0, size, cb_info))
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if result[0] != webgpu.WGPUBufferMapAsyncStatus_Success:
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raise RuntimeError(f"Failed to map buffer: [{webgpu.WGPUBufferMapAsyncStatus__enumvalues[result[0]]}] {result[1]}")
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def copy_buffer_to_buffer(dev, src, src_offset, dst, dst_offset, size):
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encoder = webgpu.wgpuDeviceCreateCommandEncoder(dev, webgpu.WGPUCommandEncoderDescriptor())
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webgpu.wgpuCommandEncoderCopyBufferToBuffer(encoder, src, src_offset, dst, dst_offset, size)
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cb = webgpu.wgpuCommandEncoderFinish(encoder, webgpu.WGPUCommandBufferDescriptor())
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webgpu.wgpuQueueSubmit(webgpu.wgpuDeviceGetQueue(dev), 1, (webgpu.WGPUCommandBuffer*1)(cb))
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webgpu.wgpuCommandBufferRelease(cb)
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webgpu.wgpuCommandEncoderRelease(encoder)
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def read_buffer(dev, buf):
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size = webgpu.wgpuBufferGetSize(buf)
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tmp_buffer = webgpu.wgpuDeviceCreateBuffer(dev, webgpu.WGPUBufferDescriptor(size=size,
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usage=webgpu.WGPUBufferUsage_CopyDst | webgpu.WGPUBufferUsage_MapRead, mappedAtCreation=False))
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copy_buffer_to_buffer(dev, buf, 0, tmp_buffer, 0, size)
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map_buffer(tmp_buffer, size)
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void_ptr = ctypes.cast(webgpu.wgpuBufferGetConstMappedRange(tmp_buffer, 0, size), ctypes.c_void_p)
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buf_copy = bytearray((ctypes.c_uint8 * size).from_address(void_ptr.value))
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webgpu.wgpuBufferUnmap(tmp_buffer)
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webgpu.wgpuBufferDestroy(tmp_buffer)
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return memoryview(buf_copy).cast("B")
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def pop_error(device):
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result: List[Any] = []
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def cb(status, err_type, msg, i2): result[:] = [from_wgpu_str(msg)]
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cb_info = create_cb_info(webgpu.WGPUPopErrorScopeCallbackInfo, webgpu.WGPUPopErrorScopeCallback, cb)
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wgpu_wait(webgpu.wgpuDevicePopErrorScopeF(device, cb_info))
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return result[0] if len(result) > 0 else ""
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def create_uniform(wgpu_device, val):
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buf = webgpu.wgpuDeviceCreateBuffer(wgpu_device,
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webgpu.WGPUBufferDescriptor(size=4, usage=webgpu.WGPUBufferUsage_Uniform | webgpu.WGPUBufferUsage_CopyDst))
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write_buffer(wgpu_device, buf, 0, val.to_bytes(4, "little") if isinstance(val, int) else struct.pack('<f', val))
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return buf
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class WebGPUProgram:
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def __init__(self, dev, name:str, lib:bytes):
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(self.dev, self.timestamp_supported) = dev
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# Creating shader module
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shader = webgpu.WGPUShaderModuleWGSLDescriptor(code=to_wgpu_str(lib.decode()),
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chain=webgpu.WGPUChainedStruct(sType=webgpu.WGPUSType_ShaderSourceWGSL))
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module = webgpu.WGPUShaderModuleDescriptor()
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module.nextInChain = ctypes.cast(ctypes.pointer(shader), ctypes.POINTER(webgpu.struct_WGPUChainedStruct))
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# Check compiler error
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webgpu.wgpuDevicePushErrorScope(self.dev, webgpu.WGPUErrorFilter_Validation)
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shader_module = webgpu.wgpuDeviceCreateShaderModule(self.dev, module)
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if err := pop_error(self.dev): raise RuntimeError(f"Shader compilation failed: {err}")
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self.name, self.lib, self.prg = name, lib, shader_module
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def __call__(self, *bufs, global_size=(1,1,1), local_size=(1,1,1), vals=(), wait=False):
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wait = wait and self.timestamp_supported
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tmp_bufs = [*bufs]
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buf_patch = False
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# WebGPU does not allow using the same buffer for input and output
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for i in range(1, len(bufs)):
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if bufs[i] == bufs[0]:
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tmp_bufs[0] = webgpu.wgpuDeviceCreateBuffer(self.dev,
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webgpu.WGPUBufferDescriptor(size=webgpu.wgpuBufferGetSize(bufs[0]), usage=webgpu.wgpuBufferGetUsage(bufs[0])))
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buf_patch = True
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# Creating bind group layout
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binding_layouts = [webgpu.WGPUBindGroupLayoutEntry(binding=0, visibility= webgpu.WGPUShaderStage_Compute,
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buffer=webgpu.WGPUBufferBindingLayout(type=webgpu.WGPUBufferBindingType_Uniform))]
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binding_layouts += [webgpu.WGPUBindGroupLayoutEntry(binding=i+1, visibility=webgpu.WGPUShaderStage_Compute,
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buffer=webgpu.WGPUBufferBindingLayout(type=webgpu.WGPUBufferBindingType_Uniform if i >= len(tmp_bufs)
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else webgpu.WGPUBufferBindingType_Storage)) for i in range(len(tmp_bufs)+len(vals))]
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bl_arr_type = webgpu.WGPUBindGroupLayoutEntry * len(binding_layouts)
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webgpu.wgpuDevicePushErrorScope(self.dev, webgpu.WGPUErrorFilter_Validation)
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bind_group_layouts = [webgpu.wgpuDeviceCreateBindGroupLayout(self.dev, webgpu.WGPUBindGroupLayoutDescriptor(
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entryCount=len(binding_layouts), entries=ctypes.cast(bl_arr_type(*binding_layouts), ctypes.POINTER(webgpu.WGPUBindGroupLayoutEntry))))]
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if bg_layout_err := pop_error(self.dev): raise RuntimeError(f"Error creating bind group layout: {bg_layout_err}")
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# Creating pipeline layout
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pipeline_layout_desc = webgpu.WGPUPipelineLayoutDescriptor(bindGroupLayoutCount=len(bind_group_layouts),
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bindGroupLayouts = (webgpu.WGPUBindGroupLayout * len(bind_group_layouts))(*bind_group_layouts))
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webgpu.wgpuDevicePushErrorScope(self.dev, webgpu.WGPUErrorFilter_Validation)
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pipeline_layout = webgpu.wgpuDeviceCreatePipelineLayout(self.dev, pipeline_layout_desc)
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if pipe_err := pop_error(self.dev): raise RuntimeError(f"Error creating pipeline layout: {pipe_err}")
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# Creating bind group
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bindings = [webgpu.WGPUBindGroupEntry(binding=0, buffer=create_uniform(self.dev, float('inf')), offset=0, size=4)]
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bindings += [webgpu.WGPUBindGroupEntry(binding=i+1, buffer=create_uniform(self.dev, x) if i >= len(tmp_bufs) else x, offset=0,
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size=4 if i >= len(tmp_bufs) else webgpu.wgpuBufferGetSize(x)) for i,x in enumerate(tuple(tmp_bufs)+vals)]
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bg_arr_type = webgpu.WGPUBindGroupEntry * len(bindings)
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bind_group_desc = webgpu.WGPUBindGroupDescriptor(layout=bind_group_layouts[0], entryCount=len(bindings), entries=bg_arr_type(*bindings))
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webgpu.wgpuDevicePushErrorScope(self.dev, webgpu.WGPUErrorFilter_Validation)
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bind_group = webgpu.wgpuDeviceCreateBindGroup(self.dev, bind_group_desc)
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if bind_err := pop_error(self.dev): raise RuntimeError(f"Error creating bind group: {bind_err}")
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# Creating compute pipeline
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compute_desc = webgpu.WGPUComputePipelineDescriptor(layout=pipeline_layout,
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compute=webgpu.WGPUComputeState(module=self.prg, entryPoint=to_wgpu_str(self.name)))
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pipeline_result: List[Any] = []
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def cb(status, compute_pipeline_impl, msg, u1, u2): pipeline_result[:] = status, compute_pipeline_impl, from_wgpu_str(msg)
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cb_info = create_cb_info(webgpu.WGPUCreateComputePipelineAsyncCallbackInfo2, webgpu.WGPUCreateComputePipelineAsyncCallback2, cb)
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webgpu.wgpuDevicePushErrorScope(self.dev, webgpu.WGPUErrorFilter_Validation)
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wgpu_wait(webgpu.wgpuDeviceCreateComputePipelineAsync2(self.dev, compute_desc, cb_info))
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if pipeline_result[0] != webgpu.WGPUCreatePipelineAsyncStatus_Success:
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raise RuntimeError(f"{webgpu.WGPUCreatePipelineAsyncStatus__enumvalues[pipeline_result[0]]}: {pipeline_result[2]}, {pop_error(self.dev)}")
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command_encoder = webgpu.wgpuDeviceCreateCommandEncoder(self.dev, webgpu.WGPUCommandEncoderDescriptor())
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comp_pass_desc = webgpu.WGPUComputePassDescriptor(nextInChain=None)
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if wait:
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query_set = webgpu.wgpuDeviceCreateQuerySet(self.dev, webgpu.WGPUQuerySetDescriptor(type=webgpu.WGPUQueryType_Timestamp, count=2))
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query_buf = webgpu.wgpuDeviceCreateBuffer(self.dev,
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webgpu.WGPUBufferDescriptor(size=16, usage=webgpu.WGPUBufferUsage_QueryResolve | webgpu.WGPUBufferUsage_CopySrc))
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comp_pass_desc.timestampWrites = ctypes.pointer(webgpu.WGPUComputePassTimestampWrites(
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querySet=query_set, beginningOfPassWriteIndex=0, endOfPassWriteIndex=1))
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# Begin compute pass
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compute_pass = webgpu.wgpuCommandEncoderBeginComputePass(command_encoder, comp_pass_desc)
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webgpu.wgpuComputePassEncoderSetPipeline(compute_pass, pipeline_result[1])
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webgpu.wgpuComputePassEncoderSetBindGroup(compute_pass, 0, bind_group, 0, None)
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webgpu.wgpuComputePassEncoderDispatchWorkgroups(compute_pass, *global_size)
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webgpu.wgpuComputePassEncoderEnd(compute_pass)
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if wait: webgpu.wgpuCommandEncoderResolveQuerySet(command_encoder, query_set, 0, 2, query_buf, 0)
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cmd_buf = webgpu.wgpuCommandEncoderFinish(command_encoder, webgpu.WGPUCommandBufferDescriptor())
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webgpu.wgpuQueueSubmit(webgpu.wgpuDeviceGetQueue(self.dev), 1, (webgpu.WGPUCommandBuffer*1)(cmd_buf))
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if buf_patch:
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copy_buffer_to_buffer(self.dev, tmp_bufs[0], 0, bufs[0], 0, webgpu.wgpuBufferGetSize(bufs[0]))
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webgpu.wgpuBufferDestroy(tmp_bufs[0])
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if wait:
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time = ((timestamps:=read_buffer(self.dev, query_buf).cast("Q").tolist())[1] - timestamps[0]) / 1e9
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webgpu.wgpuBufferDestroy(query_buf)
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webgpu.wgpuQuerySetDestroy(query_set)
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return time
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class WebGpuAllocator(Allocator):
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def __init__(self, dev): self.dev = dev
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def _alloc(self, size: int, options):
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# WebGPU buffers have to be 4-byte aligned
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return webgpu.wgpuDeviceCreateBuffer(self.dev, webgpu.WGPUBufferDescriptor(size=round_up(size, 4),
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usage=webgpu.WGPUBufferUsage_Storage | webgpu.WGPUBufferUsage_CopyDst | webgpu.WGPUBufferUsage_CopySrc))
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def _copyin(self, dest, src: memoryview):
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if src.nbytes % 4:
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padded_src = bytearray(round_up(src.nbytes, 4))
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padded_src[:src.nbytes] = src
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write_buffer(self.dev, dest, 0, padded_src if src.nbytes % 4 else src)
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def _copyout(self, dest: memoryview, src):
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buffer_data = read_buffer(self.dev, src)
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dest[:] = buffer_data[:dest.nbytes] if webgpu.wgpuBufferGetSize(src) > dest.nbytes else buffer_data
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def _free(self, opaque, options):
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webgpu.wgpuBufferDestroy(opaque)
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class WebGpuDevice(Compiled):
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def __init__(self, device:str):
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# Requesting an adapter
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adapter_result: List[Any] = []
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def adapter_cb(status, adapter, msg, _): adapter_result[:] = status, adapter, from_wgpu_str(msg)
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cb_info = create_cb_info(webgpu.WGPURequestAdapterCallbackInfo, webgpu.WGPURequestAdapterCallback, adapter_cb)
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wgpu_wait(webgpu.wgpuInstanceRequestAdapterF(instance,
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webgpu.WGPURequestAdapterOptions(powerPreference=webgpu.WGPUPowerPreference_HighPerformance), cb_info))
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if adapter_result[0] != webgpu.WGPURequestAdapterStatus_Success:
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raise RuntimeError(f"Error requesting adapter: [{webgpu.WGPURequestAdapterStatus__enumvalues[adapter_result[0]]}] {adapter_result[2]}")
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# Get supported features
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supported_features = webgpu.WGPUSupportedFeatures()
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webgpu.wgpuAdapterGetFeatures(adapter_result[1], supported_features)
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supported = [supported_features.features[i] for i in range(supported_features.featureCount)]
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features = [feat for feat in [webgpu.WGPUFeatureName_TimestampQuery, webgpu.WGPUFeatureName_ShaderF16] if feat in supported]
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dev_desc = webgpu.WGPUDeviceDescriptor(requiredFeatureCount=len(features),requiredFeatures=(webgpu.WGPUFeatureName * len(features))(*features))
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# Limits
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supported_limits = webgpu.WGPUSupportedLimits()
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webgpu.wgpuAdapterGetLimits(adapter_result[1], ctypes.cast(ctypes.pointer(supported_limits),ctypes.POINTER(webgpu.struct_WGPUSupportedLimits)))
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limits = webgpu.WGPURequiredLimits(limits=supported_limits.limits)
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dev_desc.requiredLimits = ctypes.cast(ctypes.pointer(limits),ctypes.POINTER(webgpu.struct_WGPURequiredLimits))
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# Requesting a device
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device_result: List[Any] = []
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def dev_cb(status, device_impl, msg, _): device_result[:] = status, device_impl, from_wgpu_str(msg)
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cb_info = create_cb_info(webgpu.WGPURequestDeviceCallbackInfo, webgpu.WGPURequestDeviceCallback, dev_cb)
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wgpu_wait(webgpu.wgpuAdapterRequestDeviceF(adapter_result[1], dev_desc, cb_info))
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if device_result[0] != webgpu.WGPURequestDeviceStatus_Success:
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raise RuntimeError(f"Failed to request device: [{webgpu.WGPURequestDeviceStatus__enumvalues[device_result[0]]}] {device_result[2]}")
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super().__init__(device, WebGpuAllocator(device_result[1]), WGSLRenderer(), Compiler(),
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functools.partial(WebGPUProgram, (device_result[1], webgpu.WGPUFeatureName_TimestampQuery in supported)))
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