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234 lines
12 KiB
234 lines
12 KiB
1 month ago
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from typing import Tuple, Dict, List, Optional
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from tinygrad.dtype import DType
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from tinygrad.renderer import ProgramSpec
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from tinygrad.tensor import Device, Tensor
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from tinygrad.engine.jit import TinyJit
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from tinygrad.nn.state import get_state_dict
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from tinygrad.helpers import Context
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from tinygrad.dtype import dtypes
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import json
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EXPORT_SUPPORTED_DEVICE = ["WEBGPU", "CLANG", "CUDA", "GPU"]
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def compile_net(run:TinyJit, special_names:Dict[int,str]) -> Tuple[Dict[str,str],List[Tuple[str,List[str],List[int]]],Dict[str,Tuple[int,DType,int]],Dict[str,Tensor]]:
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functions, bufs, bufs_to_save, statements, bufnum = {}, {}, {}, [], 0
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for ji in run.jit_cache:
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fxn: ProgramSpec = ji.prg.p
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functions[fxn.function_name] = fxn.src # NOTE: this assumes all with the same name are the same
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cargs = []
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for i,arg in enumerate(ji.bufs):
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key = id(arg)
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if key not in bufs:
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if key in special_names:
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bufs[key] = (special_names[key], arg.size*arg.dtype.itemsize, arg.dtype, key)
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else:
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bufs[key] = (f"buf_{bufnum}", arg.size*arg.dtype.itemsize, arg.dtype, key)
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bufnum += 1
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if i > 0: bufs_to_save[bufs[key][0]] = arg # if first usage of a buffer is not an output, and it's not a special name
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cargs.append(bufs[key][0])
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statements.append((fxn.function_name, cargs, fxn.global_size, fxn.local_size))
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return functions, statements, {name:(size, dtype, key) for (name,size,dtype,key) in bufs.values()}, bufs_to_save
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def jit_model(model, *args) -> Tuple[TinyJit,Dict[int,str]]:
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assert hasattr(model, "forward") or callable(model), "model needs a forward function"
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@TinyJit
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def run(*x):
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out = model.forward(*x) if hasattr(model, "forward") else model(*x)
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assert isinstance(out, tuple) or isinstance(out, list) or isinstance(out, Tensor), "model output must be a Tensor, tuple, or a list of Tensors for export"
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out = [out] if isinstance(out, Tensor) else out
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return [o.realize() for o in out]
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# twice to run the JIT
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for _ in range(2): the_output = run(*args)
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special_names = {}
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# hack to put the inputs back
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for (j,i),idx in run.input_replace.items():
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realized_input = args[idx].lazydata.base.realized
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run.jit_cache[j].bufs[i] = realized_input
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special_names[id(realized_input)] = f'input{idx}'
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# TODO: fetch this from the jit in self.input_replace and self.ret (hint: use get_parameters on self.ret)
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for i, output in enumerate(the_output):
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special_names[id(output.lazydata.base.realized)] = f'output{i}'
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return run, special_names
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def export_model_clang(functions:Dict[str,str], statements:Dict[str,Tuple[str,int,int]], bufs:Dict[str,Tuple[str,int,int]], bufs_to_save:Dict[str,Tensor], input_names:List[str], output_names:List[str]) -> str:
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cprog = ["#include <tgmath.h>"]
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for name,cl in bufs_to_save.items():
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weight = ''.join(["\\x%02X"%x for x in bytes(cl._buf)])
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cprog.append(f"unsigned char {name}_data[] = \"{weight}\";")
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inputs = ", ".join([f'float* {input}' for input in input_names])
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outputs = ", ".join([f'float* {output}' for output in output_names])
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cprog += [f"float {name}[{len}];" if name not in bufs_to_save else f"float *{name} = (float *){name}_data;" for name,(len,dtype,_key) in bufs.items() if name not in ['input', 'outputs']]
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cprog += list(functions.values())
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cprog += [f"void net({inputs}, {outputs}) {{"] + [f"{name}({', '.join(args)});" for (name, args, _global_size, _local_size) in statements] + ["}"]
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return '\n'.join(cprog)
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def dtype_to_js_type(dtype: DType) -> str:
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return f"{'Uint' if dtype in dtypes.uints else 'Int' if (dtype in dtypes.sints or dtype == dtypes.bool) else 'Float'}{8*dtype.itemsize}Array"
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def export_model_webgpu(functions, statements, bufs, weight_names, input_names, output_names, model_name) -> Tuple[str,int,int]:
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exported_name = "model" if model_name == None else model_name
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kernel_code = '\n\n'.join([f"const {key} = `{code.replace(key, 'main')}`;" for key, code in functions.items()])
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kernel_names = ', '.join([name for (name, _, _, _) in statements])
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create_bind_group_layouts = ",".join([
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"device.createBindGroupLayout({{entries: [{{binding: 0, visibility: GPUShaderStage.COMPUTE, buffer: {{ type: 'uniform' }}}}, {}]}})".format(
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",".join([f"{{binding: {argIdx+1}, visibility: GPUShaderStage.COMPUTE, buffer: {{ type: 'storage' }} }}" for argIdx, _ in enumerate(args)])
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)
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for _, (_, args, _, _) in enumerate(statements)
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])
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layouts = f"const layouts=[{create_bind_group_layouts}]"
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kernel_calls = '\n '.join([f"addComputePass(device, commandEncoder, pipelines[{i}], layouts[{i}], infinityBuf, [{', '.join(args)}], {global_size});" for i, (_name, args, global_size, _local_size) in enumerate(statements) ])
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_bufs = '\n '.join([f"const {name} = " + (f"createEmptyBuf(device, {size});" if _key not in weight_names else f"createWeightBuf(device, {size}, getTensorBuffer(safetensor, metadata['{weight_names[_key]}']))") + ";" for name,(size,dtype,_key) in bufs.items()])
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gpu_write_bufs = '\n '.join([f"const gpuWriteBuffer{i} = device.createBuffer({{size:{input_name}.size, usage: GPUBufferUsage.COPY_SRC | GPUBufferUsage.MAP_WRITE }});" for i,input_name in enumerate(input_names)])
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input_buffer_types = [dtype_to_js_type(bufs[inp_name][1]) for inp_name in input_names]
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output_buffer_types = [dtype_to_js_type(bufs[out_name][1]) for out_name in output_names]
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input_writers = '\n '.join([f"await gpuWriteBuffer{i}.mapAsync(GPUMapMode.WRITE);\n new {input_buffer_types[i]}(gpuWriteBuffer{i}.getMappedRange()).set(" + f'_{inp_name});' + f"\n gpuWriteBuffer{i}.unmap();\n commandEncoder.copyBufferToBuffer(gpuWriteBuffer{i}, 0, {inp_name}, 0, gpuWriteBuffer{i}.size);" for i,inp_name in enumerate(input_names)])
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gpu_read_bufs = '\n '.join([f"const gpuReadBuffer{i} = device.createBuffer({{size:{output_name}.size, usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ }});" for i,output_name in enumerate(output_names)])
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outbuf_copies = '\n '.join([f"commandEncoder.copyBufferToBuffer({output_name}, 0, gpuReadBuffer{i}, 0, output{i}.size);" for i,output_name in enumerate(output_names)])
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output_readers = '\n '.join([f"await gpuReadBuffer{i}.mapAsync(GPUMapMode.READ);\n const resultBuffer{i} = new {output_buffer_types[i]}(gpuReadBuffer{i}.size/{bufs[output_names[i]][1].itemsize});\n resultBuffer{i}.set(new {output_buffer_types[i]}(gpuReadBuffer{i}.getMappedRange()));\n gpuReadBuffer{i}.unmap();" for i in range(len(output_names))])
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output_return = '[{}]'.format(",".join([f'resultBuffer{i}' for i in range(len(output_names))]))
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return f"""
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const {exported_name} = (() => {{
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const getTensorBuffer = (safetensorBuffer, tensorMetadata) => {{
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return safetensorBuffer.subarray(...tensorMetadata.data_offsets);
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}};
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const getTensorMetadata = (safetensorBuffer) => {{
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const metadataLength = Number(new DataView(safetensorBuffer.buffer).getBigUint64(0, true));
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const metadata = JSON.parse(new TextDecoder("utf8").decode(safetensorBuffer.subarray(8, 8 + metadataLength)));
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return Object.fromEntries(Object.entries(metadata).filter(([k, v]) => k !== "__metadata__").map(([k, v]) => [k, {{...v, data_offsets: v.data_offsets.map(x => 8 + metadataLength + x)}}]));
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}};
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const createEmptyBuf = (device, size) => {{
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return device.createBuffer({{size, usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST }});
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}};
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const createInfinityUniformBuf = (device) => {{
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const size = 4;
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const buf = device.createBuffer({{
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mappedAtCreation: true,
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size,
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usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST
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}});
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new Float32Array(buf.getMappedRange())[0] = Infinity;
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buf.unmap();
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return buf;
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}};
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const createWeightBuf = (device, size, data) => {{
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const buf = device.createBuffer({{ mappedAtCreation: true, size, usage: GPUBufferUsage.STORAGE }});
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new Uint8Array(buf.getMappedRange()).set(data);
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buf.unmap();
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return buf;
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}};
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const addComputePass = (device, commandEncoder, pipeline, layout, infinityUniformBuf, bufs, workgroup) => {{
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const bindGroup = device.createBindGroup({{
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layout: layout,
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entries: [
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{{ binding: 0, resource: {{ buffer: infinityUniformBuf }} }},
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...bufs.map((buffer, index) => ({{ binding: index + 1, resource: {{ buffer }} }}))
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]
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}});
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const passEncoder = commandEncoder.beginComputePass();
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passEncoder.setPipeline(pipeline);
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passEncoder.setBindGroup(0, bindGroup);
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passEncoder.dispatchWorkgroups(...workgroup);
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passEncoder.end();
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}};
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{kernel_code}
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const setupNet = async (device, safetensor) => {{
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const metadata = getTensorMetadata(safetensor);
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const infinityBuf = createInfinityUniformBuf(device);
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{layouts}
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{_bufs}
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{gpu_write_bufs}
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{gpu_read_bufs}
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const kernels = [{kernel_names}];
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const pipelines = await Promise.all(kernels.map(async (name, i) => {{
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return await device.createComputePipelineAsync({{
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layout: device.createPipelineLayout({{
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bindGroupLayouts: [layouts[i]],
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}}),
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compute: {{
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module: device.createShaderModule({{
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code: name,
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}}),
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entryPoint: "main",
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}},
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}});
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}}))
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return async ({",".join([f"_{input_name}" for input_name in input_names])}) => {{
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const commandEncoder = device.createCommandEncoder();
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{input_writers}
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{kernel_calls}
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{outbuf_copies}
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const gpuCommands = commandEncoder.finish();
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device.queue.submit([gpuCommands]);
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{output_readers}
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return {output_return};
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}}
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}}
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const load = async (device, weight_path) => {{ return await fetch(weight_path).then(x => x.arrayBuffer()).then(x => setupNet(device, new Uint8Array(x))); }}
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return {{ load }};
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}})();
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export default {exported_name};
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"""
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def export_model(model, target:str, *inputs, model_name: Optional[str] = None):
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assert Device.DEFAULT in EXPORT_SUPPORTED_DEVICE, "only WEBGPU, CLANG, CUDA, GPU, METAL are supported"
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with Context(JIT=2): run,special_names = jit_model(model, *inputs)
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functions, statements, bufs, bufs_to_save = compile_net(run, special_names)
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state = get_state_dict(model)
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weight_names = {id(x.lazydata.base.realized): name for name, x in state.items()}
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input_names = [name for _,name in special_names.items() if "input" in name]
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output_names = [name for _,name in special_names.items() if "output" in name]
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prg = ""
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if target == "clang":
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prg = export_model_clang(functions, statements, bufs, bufs_to_save, input_names, output_names)
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elif target == "webgpu":
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prg = export_model_webgpu(functions, statements, bufs, weight_names, input_names, output_names, model_name)
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else:
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prg = json.dumps({
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"backend": Device.DEFAULT,
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"inputs": [{
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"size": bufs[name][0],
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"dtype": bufs[name][1].name
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} for name in input_names],
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"outputs": [{
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"size": bufs[name][0],
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"dtype": bufs[name][1].name
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} for name in output_names],
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"functions": functions,
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"statements": [{
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"kernel": kernel,
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"args": args,
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"global_size": global_size,
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"local_size": local_size
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} for (kernel, args, global_size, local_size) in statements],
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"buffers": {
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name: {
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"size": size,
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"dtype": dtype.name,
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"id": weight_names[_key] if _key in weight_names else ""
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} for name, (size,dtype,_key) in bufs.items() if name not in ["input", "outputs"]
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}
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})
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return prg, {input:bufs[input][0] for input in input_names}, {output:bufs[output][0] for output in output_names}, state
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