StarPilot/tinygrad_repo/examples/webgpu/stable_diffusion/compile.py

import os
from extra.export_model import compile_net, jit_model, dtype_to_js_type
from extra.f16_decompress import u32_to_f16
from examples.stable_diffusion import StableDiffusion
from tinygrad.nn.state import get_state_dict, safe_save, safe_load_metadata, torch_load, load_state_dict
from tinygrad.tensor import Tensor
from tinygrad import dtypes
from tinygrad.helpers import DEV, fetch
from typing import NamedTuple, Any, List
import requests
import argparse
import numpy as np

def convert_f32_to_f16(input_file, output_file):
  with open(input_file, 'rb') as f:
    metadata_length_bytes = f.read(8)
    metadata_length = int.from_bytes(metadata_length_bytes, byteorder='little', signed=False)
    metadata_json_bytes = f.read(metadata_length)
    float32_values = np.fromfile(f, dtype=np.float32)

  first_text_model_offset = 3772703308
  num_elements = int((first_text_model_offset)/4)
  front_float16_values = float32_values[:num_elements].astype(np.float16)
  rest_float32_values = float32_values[num_elements:]

  with open(output_file, 'wb') as f:
    f.write(metadata_length_bytes)
    f.write(metadata_json_bytes)
    front_float16_values.tofile(f)
    rest_float32_values.tofile(f)

def split_safetensor(fn):
  _, data_start, metadata = safe_load_metadata(fn)
  text_model_offset = 3772703308
  chunk_size = 536870912

  for k in metadata:
    # safetensor is in fp16, except for text moel
    if (metadata[k]["data_offsets"][0] < text_model_offset):
      metadata[k]["data_offsets"][0] = int(metadata[k]["data_offsets"][0]/2)
      metadata[k]["data_offsets"][1] = int(metadata[k]["data_offsets"][1]/2)

  last_offset = 0
  part_end_offsets = []

  for k in metadata:
    offset = metadata[k]['data_offsets'][0]

    if offset == text_model_offset:
      break

    part_offset = offset - last_offset

    if (part_offset >= chunk_size):
      part_end_offsets.append(data_start+offset)
      last_offset = offset

  text_model_start = int(text_model_offset/2)
  net_bytes = bytes(open(fn, 'rb').read())
  part_end_offsets.append(text_model_start+data_start)
  cur_pos = 0

  for i, end_pos in enumerate(part_end_offsets):
    with open(os.path.join(os.path.dirname(__file__), f'./net_part{i}.safetensors'), "wb+") as f:
      f.write(net_bytes[cur_pos:end_pos])
      cur_pos = end_pos

  with open(os.path.join(os.path.dirname(__file__), f'./net_textmodel.safetensors'), "wb+") as f:
    f.write(net_bytes[text_model_start+data_start:])

  return part_end_offsets

def fetch_dep(file, url):
  with open(file, "w", encoding="utf-8") as f:
    f.write(requests.get(url).text.replace("https://huggingface.co/wpmed/tinygrad-sd-f16/raw/main/bpe_simple_vocab_16e6.mjs", "./bpe_simple_vocab_16e6.mjs"))

if __name__ == "__main__":
  fetch_dep(os.path.join(os.path.dirname(__file__), "clip_tokenizer.js"), "https://huggingface.co/wpmed/tinygrad-sd-f16/raw/main/clip_tokenizer.js")
  fetch_dep(os.path.join(os.path.dirname(__file__), "bpe_simple_vocab_16e6.mjs"), "https://huggingface.co/wpmed/tinygrad-sd-f16/raw/main/bpe_simple_vocab_16e6.mjs")
  parser = argparse.ArgumentParser(description='Run Stable Diffusion', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  parser.add_argument('--remoteweights', action='store_true', help="Use safetensors from Huggingface, or from local")
  args = parser.parse_args()
  DEV.value = "WEBGPU"

  model = StableDiffusion()

  # load in weights
  load_state_dict(model, torch_load(fetch('https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt', 'sd-v1-4.ckpt'))['state_dict'], strict=False)

  class Step(NamedTuple):
    name: str = ""
    input: List[Tensor] = []
    forward: Any = None

  sub_steps = [
    Step(name = "textModel", input = [Tensor.randint(1, 77, low=0, high=49408, dtype=dtypes.int32)], forward = model.cond_stage_model.transformer.text_model),
    Step(name = "diffusor", input = [Tensor.randn(1, 77, 768), Tensor.randn(1, 77, 768), Tensor.randn(1,4,64,64), Tensor.rand(1), Tensor.randn(1), Tensor.randn(1), Tensor.randn(1)], forward = model),
    Step(name = "decoder", input = [Tensor.randn(1,4,64,64)], forward = model.decode),
    Step(name = "f16tof32", input = [Tensor.randn(2097120, dtype=dtypes.uint32)], forward = u32_to_f16)
  ]

  prg = ""

  def fixup_code(code, key):
    code = code.replace(key, 'main')\
      .replace("var<uniform> INFINITY : f32;\n", "fn inf(a: f32) -> f32 { return a/0.0; }\n")\
      .replace("@group(0) @binding(0)", "")\
      .replace("INFINITY", "inf(1.0)")

    for i in range(1,9): code = code.replace(f"binding({i})", f"binding({i-1})")
    return code

  def compile_step(model, step: Step):
    linear, output_bufs = jit_model(step, *step.input)
    functions, statements, bufs, _ = compile_net(linear, output_bufs)
    state = get_state_dict(model)
    weights = {(id(b), b.offset, b.size, b.dtype): name for name, x in state.items() if (b:=x.uop.base.realized) is not None}
    kernel_code = '\n\n'.join([f"const {key} = `{fixup_code(code, key)}`;" for key, code in functions.items()])
    kernel_names = ', '.join([name for (name, _, _, _) in statements])
    input_names = [f"input{i}" for i in range(len(step.input))]
    output_names = [f"output{i}" for i in range(len(output_bufs))]
    input_buf_types = [dtype_to_js_type(bufs[inp_name][1]) for inp_name in input_names]
    output_buf_types = [dtype_to_js_type(bufs[out_name][1]) for out_name in output_names]
    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) ])
    exported_bufs =  '\n    '.join([f"const {name} = " + (f"createEmptyBuf(device, {size});" if _key not in weights else f"createWeightBuf(device, {size}, getTensorBuffer(safetensor, metadata['{weights[_key]}'], '{weights[_key]}'))") + ";"  for name,(size,dtype,_key) in bufs.items()])
    gpu_write_bufs =  '\n    '.join([f"const gpuWriteBuffer{i} = device.createBuffer({{size:input{i}.size, usage: GPUBufferUsage.COPY_SRC | GPUBufferUsage.MAP_WRITE }});" for i in range(len(input_names))])
    input_writer = '\n    '.join([f"await gpuWriteBuffer{i}.mapAsync(GPUMapMode.WRITE);\n    new {input_buf_types[i]}(gpuWriteBuffer{i}.getMappedRange()).set(" + f'data{i});' + f"\n    gpuWriteBuffer{i}.unmap();\ncommandEncoder.copyBufferToBuffer(gpuWriteBuffer{i}, 0, input{i}, 0, gpuWriteBuffer{i}.size);"  for i,_ in enumerate(input_names)])
    return f"""\n    var {step.name} = function() {{

    {kernel_code}

    return {{
      "setup": async (device, safetensor) => {{
        const metadata = safetensor ? getTensorMetadata(safetensor[0]) : null;

        {exported_bufs}

        {gpu_write_bufs}
        const gpuReadBuffer = device.createBuffer({{ size: output0.size, usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ }});

        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'data{i}' for i in range(len(input_names))])}) => {{
            const commandEncoder = device.createCommandEncoder();

            {input_writer}

            {kernel_calls}
            commandEncoder.copyBufferToBuffer(output0, 0, gpuReadBuffer, 0, output0.size);
            const gpuCommands = commandEncoder.finish();
            device.queue.submit([gpuCommands]);

            await gpuReadBuffer.mapAsync(GPUMapMode.READ);
            const resultBuffer = new {output_buf_types[0]}(gpuReadBuffer.size/{bufs[output_names[0]][1].itemsize});
            resultBuffer.set(new {output_buf_types[0]}(gpuReadBuffer.getMappedRange()));
            gpuReadBuffer.unmap();
            return resultBuffer;
        }}
      }}
    }}
  }}
  """

  for step in sub_steps:
    print(f'Executing step={step.name}')
    prg += compile_step(model, step)

    if step.name == "diffusor":
      if args.remoteweights:
        base_url = "https://huggingface.co/wpmed/stable-diffusion-f16-new/resolve/main"
      else:
        state = get_state_dict(model)
        safe_save(state, os.path.join(os.path.dirname(__file__), "net.safetensors"))
        convert_f32_to_f16(os.path.join(os.path.dirname(__file__), "./net.safetensors"), os.path.join(os.path.dirname(__file__), "./net_conv.safetensors"))
        split_safetensor(os.path.join(os.path.dirname(__file__), "./net_conv.safetensors"))
        os.remove(os.path.join(os.path.dirname(__file__), "net.safetensors"))
        os.remove(os.path.join(os.path.dirname(__file__), "net_conv.safetensors"))
        base_url = "."

  prekernel = f"""
    window.MODEL_BASE_URL= "{base_url}";
    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 = (safetensorParts, tensorMetadata, key) => {{
    let selectedPart = 0;
    let counter = 0;
    let partStartOffsets = [1131408336, 2227518416, 3308987856, 4265298864];
    let correctedOffsets = tensorMetadata.data_offsets;
    let prev_offset = 0;

    for (let start of partStartOffsets) {{
      prev_offset = (counter == 0) ? 0 : partStartOffsets[counter-1];

      if (tensorMetadata.data_offsets[0] < start) {{
        selectedPart = counter;
        correctedOffsets = [correctedOffsets[0]-prev_offset, correctedOffsets[1]-prev_offset];
        break;
      }}

      counter++;
    }}

    return safetensorParts[selectedPart].subarray(...correctedOffsets);
  }}

  const getWeight = (safetensors, key) => {{
    let uint8Data = getTensorBuffer(safetensors, getTensorMetadata(safetensors[0])[key], key);
    return new Float32Array(uint8Data.buffer, uint8Data.byteOffset, uint8Data.byteLength / Float32Array.BYTES_PER_ELEMENT);
  }}

  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();
  }};"""

  with open(os.path.join(os.path.dirname(__file__), "net.js"), "w") as text_file:
    text_file.write(prekernel + prg)