180 lines
9.6 KiB
Python
180 lines
9.6 KiB
Python
from typing import Tuple, Dict, List
|
|
from tinygrad.helpers import DType
|
|
from tinygrad.tensor import Device, Tensor
|
|
from tinygrad.jit import TinyJit
|
|
from tinygrad.nn.state import get_state_dict
|
|
import json
|
|
|
|
EXPORT_SUPPORTED_DEVICE = ["WEBGPU", "CLANG", "CUDA", "GPU", "METAL"]
|
|
|
|
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]]:
|
|
functions, bufs, bufs_to_save, statements, bufnum = {}, {}, {}, [], 0
|
|
for fxn,args,var_vals in run.jit_cache:
|
|
assert not var_vals, "symbolic shape is not supported"
|
|
functions[fxn.name] = fxn.prg # NOTE: this assumes all with the same name are the same
|
|
cargs = []
|
|
for i,arg in enumerate(args):
|
|
key = id(arg)
|
|
if key not in bufs:
|
|
if key in special_names:
|
|
bufs[key] = (special_names[key], arg._memsz, arg.dtype, key)
|
|
else:
|
|
bufs[key] = (f"buf_{bufnum}", arg._memsz, arg.dtype, key)
|
|
bufnum += 1
|
|
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
|
|
cargs.append(bufs[key][0])
|
|
statements.append((fxn.name, cargs, fxn.global_size, fxn.local_size))
|
|
|
|
return functions, statements, {name:(size, dtype, key) for (name,size,dtype,key) in bufs.values()}, bufs_to_save
|
|
|
|
def jit_model(model, *args) -> Tuple[TinyJit,Dict[int,str]]:
|
|
assert hasattr(model, "forward") or callable(model), "model needs a forward function"
|
|
@TinyJit
|
|
def run(*x):
|
|
out = model.forward(*x) if hasattr(model, "forward") else model(*x)
|
|
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"
|
|
out = [out] if isinstance(out, Tensor) else out
|
|
return [o.realize() for o in out]
|
|
|
|
# twice to run the JIT
|
|
for _ in range(2): the_output = run(*args)
|
|
special_names = {}
|
|
|
|
# hack to put the inputs back
|
|
for (j,i),idx in run.input_replace.items():
|
|
realized_input = args[idx[0]].lazydata.realized
|
|
run.jit_cache[j][1][i] = realized_input
|
|
special_names[id(realized_input)] = f'input{idx[0]}'
|
|
|
|
# TODO: fetch this from the jit in self.input_replace and self.ret (hint: use get_parameters on self.ret)
|
|
for i, output in enumerate(the_output):
|
|
special_names[id(output.lazydata.realized)] = f'output{i}'
|
|
return run, special_names
|
|
|
|
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:
|
|
from tinygrad.runtime.ops_clang import CLANG_PROGRAM_HEADER
|
|
cprog = [CLANG_PROGRAM_HEADER]
|
|
|
|
for name,cl in bufs_to_save.items():
|
|
weight = ''.join(["\\x%02X"%x for x in bytes(cl._buf)])
|
|
cprog.append(f"unsigned char {name}_data[] = \"{weight}\";")
|
|
|
|
inputs = ", ".join([f'float* {input}' for input in input_names])
|
|
outputs = ", ".join([f'float* {output}' for output in output_names])
|
|
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']]
|
|
cprog += list(functions.values())
|
|
cprog += [f"void net({inputs}, {outputs}) {{"] + [f"{name}({', '.join(args)});" for (name, args, _global_size, _local_size) in statements] + ["}"]
|
|
return '\n'.join(cprog)
|
|
|
|
def export_model_webgpu(functions, statements, bufs, bufs_to_save, weight_names, input_names, output_names) -> Tuple[str,int,int]:
|
|
kernel_code = '\n\n'.join([f"const {key} = `{code.replace(key, 'main')}`;" for key, code in functions.items()])
|
|
kernel_names = ', '.join([name for (name, _args, _global_size, _local_size) in statements])
|
|
kernel_calls = '\n '.join([f"addComputePass(device, commandEncoder, piplines[{i}], [{', '.join(args)}], {global_size});" for i, (_name, args, global_size, _local_size) in enumerate(statements) ])
|
|
_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()])
|
|
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)])
|
|
input_writers = '\n '.join([f"await gpuWriteBuffer{i}.mapAsync(GPUMapMode.WRITE);\n new Float32Array(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)])
|
|
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)])
|
|
outbuf_copies = '\n '.join([f"commandEncoder.copyBufferToBuffer({output_name}, 0, gpuReadBuffer{i}, 0, output{i}.size);" for i,output_name in enumerate(output_names)])
|
|
output_readers = '\n '.join([f"await gpuReadBuffer{i}.mapAsync(GPUMapMode.READ);\n const resultBuffer{i} = new Float32Array(gpuReadBuffer{i}.size);\n resultBuffer{i}.set(new Float32Array(gpuReadBuffer{i}.getMappedRange()));\n gpuReadBuffer{i}.unmap();" for i in range(len(output_names))])
|
|
output_return = '[{}]'.format(",".join([f'resultBuffer{i}' for i in range(len(output_names))]))
|
|
return f"""
|
|
const getTensorMetadata = (safetensorBuffer) => {{
|
|
const metadataLength = Number(new DataView(safetensorBuffer.buffer).getBigUint64(0, true));
|
|
const metadata = JSON.parse(new TextDecoder("utf8").decode(safetensorBuffer.subarray(8, 8 + metadataLength)));
|
|
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)}}]));
|
|
}};
|
|
|
|
const getTensorBuffer = (safetensorBuffer, tensorMetadata) => {{
|
|
return safetensorBuffer.subarray(...tensorMetadata.data_offsets);
|
|
}}
|
|
|
|
const createEmptyBuf = (device, size) => {{
|
|
return device.createBuffer({{size, usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST }});
|
|
}};
|
|
|
|
const createWeightBuf = (device, size, data) => {{
|
|
const buf = device.createBuffer({{ mappedAtCreation: true, size, usage: GPUBufferUsage.STORAGE }});
|
|
new Uint8Array(buf.getMappedRange()).set(data);
|
|
buf.unmap();
|
|
return buf;
|
|
}};
|
|
|
|
const addComputePass = (device, commandEncoder, pipeline, bufs, workgroup) => {{
|
|
const bindGroup = device.createBindGroup({{layout: pipeline.getBindGroupLayout(0), entries: bufs.map((buffer, index) => ({{ binding: index, resource: {{ buffer }} }}))}});
|
|
const passEncoder = commandEncoder.beginComputePass();
|
|
passEncoder.setPipeline(pipeline);
|
|
passEncoder.setBindGroup(0, bindGroup);
|
|
passEncoder.dispatchWorkgroups(...workgroup);
|
|
passEncoder.end();
|
|
}};
|
|
|
|
{kernel_code}
|
|
|
|
const setupNet = async (device, safetensor) => {{
|
|
const metadata = getTensorMetadata(safetensor);
|
|
|
|
{_bufs}
|
|
|
|
{gpu_write_bufs}
|
|
|
|
{gpu_read_bufs}
|
|
|
|
const kernels = [{kernel_names}];
|
|
const piplines = await Promise.all(kernels.map(name => device.createComputePipelineAsync({{layout: "auto", compute: {{ module: device.createShaderModule({{ code: name }}), entryPoint: "main" }}}})));
|
|
|
|
return async ({",".join([f"_{input_name}" for input_name in input_names])}) => {{
|
|
const commandEncoder = device.createCommandEncoder();
|
|
{input_writers}
|
|
{kernel_calls}
|
|
{outbuf_copies}
|
|
const gpuCommands = commandEncoder.finish();
|
|
device.queue.submit([gpuCommands]);
|
|
|
|
{output_readers}
|
|
return {output_return};
|
|
}}
|
|
}}
|
|
""" + f"\n\nconst loadNet = async (device) => {{ return await fetch('net.safetensors').then(x => x.arrayBuffer()).then(x => setupNet(device, new Uint8Array(x))); }}"
|
|
|
|
def export_model(model, target:str, *inputs):
|
|
assert Device.DEFAULT in EXPORT_SUPPORTED_DEVICE, "only WEBGPU, CLANG, CUDA, GPU, METAL are supported"
|
|
run,special_names = jit_model(model, *inputs)
|
|
functions, statements, bufs, bufs_to_save = compile_net(run, special_names)
|
|
state = get_state_dict(model)
|
|
weight_names = {id(x.lazydata.realized): name for name, x in state.items()}
|
|
input_names = [name for _,name in special_names.items() if "input" in name]
|
|
output_names = [name for _,name in special_names.items() if "output" in name]
|
|
prg = ""
|
|
if target == "clang":
|
|
prg = export_model_clang(functions, statements, bufs, bufs_to_save, input_names, output_names)
|
|
elif target == "webgpu":
|
|
prg = export_model_webgpu(functions, statements, bufs, bufs_to_save, weight_names, input_names, output_names)
|
|
else:
|
|
prg = json.dumps({
|
|
"backend": Device.DEFAULT,
|
|
"inputs": [{
|
|
"size": bufs[name][0],
|
|
"dtype": bufs[name][1].name
|
|
} for name in input_names],
|
|
"outputs": [{
|
|
"size": bufs[name][0],
|
|
"dtype": bufs[name][1].name
|
|
} for name in output_names],
|
|
"functions": functions,
|
|
"statements": [{
|
|
"kernel": kernel,
|
|
"args": args,
|
|
"global_size": global_size,
|
|
"local_size": local_size
|
|
} for (kernel, args, global_size, local_size) in statements],
|
|
"buffers": {
|
|
name: {
|
|
"size": size,
|
|
"dtype": dtype.name,
|
|
"id": weight_names[_key] if _key in weight_names else ""
|
|
} for name, (size,dtype,_key) in bufs.items() if name not in ["input", "outputs"]
|
|
}
|
|
})
|
|
|
|
return prg, {input:bufs[input][0] for input in input_names}, {output:bufs[output][0] for output in output_names}, state
|