stable-diffusion.cpp/scripts/convert_fp8_scale_to_bf16.py at master · unslothai/stable-diffusion.cpp · GitHub

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#!/usr/bin/env python

import argparse

import json

import math

import os

import struct

from collections import Counter

from pathlib import Path

import torch

from safetensors import safe_open

FLOAT_DTYPES = {

"BF16",

"F16",

"F32",

"F64",

"F8_E4M3",

"F8_E4M3FN",

"F8_E5M2",

}

FP8_DTYPES = {

"F8_E4M3",

"F8_E4M3FN",

"F8_E5M2",

}

DTYPE_SIZES = {

"BOOL": 1,

"U8": 1,

"I8": 1,

"F8_E4M3": 1,

"F8_E4M3FN": 1,

"F8_E5M2": 1,

"U16": 2,

"I16": 2,

"F16": 2,

"BF16": 2,

"U32": 4,

"I32": 4,

"F32": 4,

"U64": 8,

"I64": 8,

"F64": 8,

}

def read_safetensors_header(path: Path):

with path.open("rb") as f:

header_len = struct.unpack("<Q", f.read(8))[0]

header = f.read(header_len).decode("utf-8").rstrip()

return json.loads(header)

def numel(shape):

return math.prod(shape) if shape else 1

def scale_key_for_weight(name: str):

if name.endswith(".weight"):

return name[:-len(".weight")] + ".weight_scale"

if name.endswith("weight"):

return name + "_scale"

return None

def tensor_nbytes(dtype: str, shape):

return numel(shape) * DTYPE_SIZES[dtype]

def build_output_plan(header):

entries = {k: v for k, v in header.items() if k != "__metadata__"}

paired_scale_keys = set()

plan = []

for name, info in entries.items():

scale_key = scale_key_for_weight(name)

if info["dtype"] in FP8_DTYPES and scale_key in entries:

paired_scale_keys.add(scale_key)

for name, info in entries.items():

if name in paired_scale_keys:

continue

dtype = info["dtype"]

shape = info["shape"]

scale_key = scale_key_for_weight(name)

if dtype in FP8_DTYPES and scale_key in entries:

scale_info = entries[scale_key]

plan.append(

{

"name": name,

"source_dtype": dtype,

"output_dtype": "BF16",

"shape": shape,

"mode": "fp8_scaled_weight",

"scale_key": scale_key,

}

)

continue

if dtype in FLOAT_DTYPES:

plan.append(

{

"name": name,

"source_dtype": dtype,

"output_dtype": "BF16",

"shape": shape,

"mode": "float_to_bf16",

}

)

else:

plan.append(

{

"name": name,

"source_dtype": dtype,

"output_dtype": dtype,

"shape": shape,

"mode": "copy",

}

)

metadata = dict(header.get("__metadata__", {}) or {})

metadata["format"] = "pt"

metadata["conversion"] = "fp8_weight_scale_to_bf16"

output_header = {"__metadata__": metadata}

offset = 0

for item in plan:

size = tensor_nbytes(item["output_dtype"], item["shape"])

output_header[item["name"]] = {

"dtype": item["output_dtype"],

"shape": item["shape"],

"data_offsets": [offset, offset + size],

}

offset += size

return plan, output_header, offset

def write_tensor_bytes(out, tensor):

tensor = tensor.detach().cpu().contiguous()

if tensor.numel() == 0:

return

if tensor.dtype == torch.bfloat16:

tensor.view(torch.uint16).numpy().tofile(out)

elif tensor.dtype in (getattr(torch, "float8_e4m3fn", None), getattr(torch, "float8_e5m2", None)):

tensor.view(torch.uint8).numpy().tofile(out)

else:

tensor.numpy().tofile(out)

def scale_view_for_chunk(scale, chunk, first_dim_start=0, first_dim_end=None):

scale = scale.to(torch.float32)

if scale.numel() == 1:

return scale.reshape((1,) * chunk.ndim)

if chunk.ndim > 0 and scale.ndim == 1:

if first_dim_end is not None and scale.shape[0] >= first_dim_end:

scale = scale[first_dim_start:first_dim_end]

if scale.shape[0] == chunk.shape[0]:

return scale.reshape((scale.shape[0],) + (1,) * (chunk.ndim - 1))

return scale

def write_scaled_fp8_weight(out, weight, scale, chunk_rows):

if weight.ndim == 0:

result = weight.to(torch.float32) * scale_view_for_chunk(scale, weight)

write_tensor_bytes(out, result.to(torch.bfloat16))

return

rows = weight.shape[0]

for start in range(0, rows, chunk_rows):

end = min(start + chunk_rows, rows)

chunk = weight[start:end].to(torch.float32)

scale_view = scale_view_for_chunk(scale, chunk, start, end)

result = chunk * scale_view

write_tensor_bytes(out, result.to(torch.bfloat16))

def write_float_as_bf16(out, tensor, chunk_rows):

if tensor.dtype == torch.bfloat16:

write_tensor_bytes(out, tensor)

return

if tensor.ndim == 0:

write_tensor_bytes(out, tensor.to(torch.bfloat16))

return

rows = tensor.shape[0]

for start in range(0, rows, chunk_rows):

end = min(start + chunk_rows, rows)

write_tensor_bytes(out, tensor[start:end].to(torch.bfloat16))

def convert(input_path: Path, output_path: Path, chunk_rows: int, dry_run: bool):

header = read_safetensors_header(input_path)

plan, output_header, data_size = build_output_plan(header)

source_counts = Counter(item["source_dtype"] for item in plan)

output_counts = Counter(item["output_dtype"] for item in plan)

scaled_count = sum(item["mode"] == "fp8_scaled_weight" for item in plan)

dropped_scales = sum(item["mode"] == "fp8_scaled_weight" for item in plan)

header_bytes = json.dumps(output_header, separators=(",", ":")).encode("utf-8")

expected_size = 8 + len(header_bytes) + data_size

print(f"input: {input_path}")

print(f"output: {output_path}")

print(f"tensors written: {len(plan)}")

print(f"scaled fp8 weights dequantized: {scaled_count}")

print(f"weight_scale tensors dropped: {dropped_scales}")

print(f"source dtypes: {dict(sorted(source_counts.items()))}")

print(f"output dtypes: {dict(sorted(output_counts.items()))}")

print(f"expected output size: {expected_size / (1024 ** 3):.2f} GiB")

if dry_run:

return

if output_path.exists():

raise FileExistsError(f"{output_path} already exists; pass --overwrite to replace it")

tmp_path = output_path.with_suffix(output_path.suffix + ".tmp")

if tmp_path.exists():

raise FileExistsError(f"{tmp_path} already exists; remove it or choose another output")

with safe_open(str(input_path), framework="pt", device="cpu") as sf, tmp_path.open("wb") as out:

out.write(struct.pack("<Q", len(header_bytes)))

out.write(header_bytes)

for index, item in enumerate(plan, 1):

name = item["name"]

print(f"[{index:04d}/{len(plan):04d}] {name} -> {item['output_dtype']}")

tensor = sf.get_tensor(name)

if item["mode"] == "fp8_scaled_weight":

scale = sf.get_tensor(item["scale_key"])

write_scaled_fp8_weight(out, tensor, scale, chunk_rows)

elif item["mode"] == "float_to_bf16":

write_float_as_bf16(out, tensor, chunk_rows)

else:

write_tensor_bytes(out, tensor)

actual_size = out.tell()

if actual_size != expected_size:

tmp_path.unlink(missing_ok=True)

raise RuntimeError(f"wrote {actual_size} bytes, expected {expected_size} bytes")

tmp_path.replace(output_path)

print("done")

def main():

parser = argparse.ArgumentParser(

description="Convert an fp8 safetensors checkpoint with weight_scale tensors to bf16."

)

parser.add_argument("--input", default="ideogram4_fp8.safetensors", type=Path)

parser.add_argument("--output", default="ideogram4_bf16.safetensors", type=Path)

parser.add_argument("--chunk-rows", default=1024, type=int)

parser.add_argument("--dry-run", action="store_true")

parser.add_argument("--overwrite", action="store_true")

args = parser.parse_args()

input_path = args.input.resolve()

output_path = args.output.resolve()

if args.chunk_rows < 1:

raise ValueError("--chunk-rows must be >= 1")

if not input_path.exists():

raise FileNotFoundError(input_path)

if args.overwrite and output_path.exists():

output_path.unlink()

convert(input_path, output_path, args.chunk_rows, args.dry_run)

if __name__ == "__main__":

main()

stable-diffusion.cpp/scripts/convert_fp8_scale_to_bf16.py at master · unslothai/stable-diffusion.cpp · GitHub

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