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https://github.com/deepseek-ai/DeepSeek-V3.git
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Update fp8_cast_bf16.py
Type Hints & Path Management:
Added comprehensive type annotations
Used pathlib.Path for safer path handling
Enhanced Error Handling:
Structured exception handling throughout
Clear error messages with context
Safe resource cleanup
Memory Management:
LRU cache implementation with OrderedDict
Configurable cache size
Explicit GPU memory cleanup
Logging System:
Configurable logging levels
Detailed progress tracking
Structured error reporting
Code Organization:
Split into focused, testable functions
Clear separation of concerns
Documented public methods
Validation & Safety:
Input path validation
Weight type checking
Clone tensors to prevent reference issues
Performance:
Optimized file loading with LRU cache
Batched tensor processing
Asynchronous CUDA operations
Metadata & Traceability:
Added conversion metadata to output files
Preserved original index structure
Enhanced output index information
Configuration:
Centralized constants
Device-aware execution (CUDA/CPU)
Progress Tracking:
Nested progress bars
Detailed file processing status
This commit is contained in:
Cristian Cezar Moisés
GitHub
parent
a26fca4a41
commit
eee820cc36
@ -1,112 +1,220 @@
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import os
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import json
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import logging
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from argparse import ArgumentParser
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from glob import glob
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from tqdm import tqdm
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from pathlib import Path
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from typing import Dict, Tuple, Optional
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from collections import OrderedDict
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from tqdm import tqdm
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import torch
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from safetensors.torch import load_file, save_file
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from kernel import weight_dequant
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def main(fp8_path, bf16_path):
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"""
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Converts FP8 weights to BF16 and saves the converted weights.
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# Configure logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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This function reads FP8 weights from the specified directory, converts them to BF16,
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and saves the converted weights to another specified directory. It also updates the
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model index file to reflect the changes.
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# Constants
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CACHE_SIZE = 2 # Number of safetensors files to keep in memory
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TORCH_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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VALID_WEIGHT_TYPES = (torch.float8_e4m3fn, torch.float8_e5m2)
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Args:
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fp8_path (str): The path to the directory containing the FP8 weights and model index file.
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bf16_path (str): The path to the directory where the converted BF16 weights will be saved.
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def validate_paths(fp8_path: Path, bf16_path: Path) -> None:
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"""Validate input and output paths."""
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if not fp8_path.is_dir():
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raise ValueError(f"Input directory {fp8_path} does not exist")
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if not (fp8_path / "model.safetensors.index.json").exists():
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raise FileNotFoundError("Missing model index file in input directory")
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Raises:
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KeyError: If a required scale_inv tensor is missing for a weight.
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bf16_path.mkdir(parents=True, exist_ok=True)
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if not os.access(bf16_path, os.W_OK):
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raise PermissionError(f"No write permission for output directory {bf16_path}")
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Notes:
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- The function assumes that the FP8 weights are stored in safetensor files.
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- The function caches loaded safetensor files to optimize memory usage.
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- The function updates the model index file to remove references to scale_inv tensors.
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"""
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torch.set_default_dtype(torch.bfloat16)
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os.makedirs(bf16_path, exist_ok=True)
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model_index_file = os.path.join(fp8_path, "model.safetensors.index.json")
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with open(model_index_file, "r") as f:
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def load_model_index(fp8_path: Path) -> Tuple[Dict, Dict]:
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"""Load and validate model index file."""
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index_path = fp8_path / "model.safetensors.index.json"
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try:
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with open(index_path, "r") as f:
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model_index = json.load(f)
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weight_map = model_index["weight_map"]
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return model_index, model_index["weight_map"].copy()
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except (json.JSONDecodeError, KeyError) as e:
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logger.error(f"Invalid model index file: {str(e)}")
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raise
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# Cache for loaded safetensor files
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loaded_files = {}
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fp8_weight_names = []
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# Helper function to get tensor from the correct file
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def get_tensor(tensor_name):
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"""
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Retrieves a tensor from the cached safetensor files or loads it from disk if not cached.
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Args:
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tensor_name (str): The name of the tensor to retrieve.
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Returns:
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torch.Tensor: The retrieved tensor.
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Raises:
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KeyError: If the tensor does not exist in the safetensor file.
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"""
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file_name = weight_map[tensor_name]
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if file_name not in loaded_files:
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file_path = os.path.join(fp8_path, file_name)
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loaded_files[file_name] = load_file(file_path, device="cuda")
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return loaded_files[file_name][tensor_name]
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safetensor_files = list(glob(os.path.join(fp8_path, "*.safetensors")))
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safetensor_files.sort()
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for safetensor_file in tqdm(safetensor_files):
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file_name = os.path.basename(safetensor_file)
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current_state_dict = load_file(safetensor_file, device="cuda")
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loaded_files[file_name] = current_state_dict
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new_state_dict = {}
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for weight_name, weight in current_state_dict.items():
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def process_weight(
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weight_name: str,
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weight: torch.Tensor,
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weight_map: Dict[str, str],
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file_cache: OrderedDict,
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fp8_path: Path,
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fp8_weight_names: list
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) -> Optional[torch.Tensor]:
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"""Process a single weight tensor."""
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if weight_name.endswith("_scale_inv"):
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continue
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elif weight.element_size() == 1: # FP8 weight
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return None
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if weight.dtype in VALID_WEIGHT_TYPES and weight.element_size() == 1:
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return handle_fp8_weight(weight_name, weight, weight_map, file_cache, fp8_path, fp8_weight_names)
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return weight.clone()
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def handle_fp8_weight(
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weight_name: str,
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weight: torch.Tensor,
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weight_map: Dict[str, str],
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file_cache: OrderedDict,
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fp8_path: Path,
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fp8_weight_names: list
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) -> torch.Tensor:
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"""Handle FP8 weight conversion to BF16."""
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scale_inv_name = f"{weight_name}_scale_inv"
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try:
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# Get scale_inv from the correct file
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scale_inv = get_tensor(scale_inv_name)
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scale_inv = load_tensor_from_cache(scale_inv_name, weight_map, file_cache, fp8_path)
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fp8_weight_names.append(weight_name)
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new_state_dict[weight_name] = weight_dequant(weight, scale_inv)
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return weight_dequant(weight, scale_inv)
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except KeyError:
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print(f"Warning: Missing scale_inv tensor for {weight_name}, skipping conversion")
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new_state_dict[weight_name] = weight
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else:
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new_state_dict[weight_name] = weight
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logger.warning(f"Missing scale_inv tensor for {weight_name}, using original weight")
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return weight.clone()
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except Exception as e:
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logger.error(f"Error processing {weight_name}: {str(e)}")
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raise
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new_safetensor_file = os.path.join(bf16_path, file_name)
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save_file(new_state_dict, new_safetensor_file)
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def load_tensor_from_cache(
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tensor_name: str,
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weight_map: Dict[str, str],
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file_cache: OrderedDict,
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fp8_path: Path
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) -> torch.Tensor:
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"""Load tensor from cached files or disk."""
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if tensor_name not in weight_map:
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raise KeyError(f"Tensor {tensor_name} not found in weight map")
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# Memory management: keep only the 2 most recently used files
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if len(loaded_files) > 2:
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oldest_file = next(iter(loaded_files))
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del loaded_files[oldest_file]
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file_name = weight_map[tensor_name]
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if file_name not in file_cache:
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load_file_to_cache(file_name, file_cache, fp8_path)
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return file_cache[file_name][tensor_name]
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def load_file_to_cache(file_name: str, file_cache: OrderedDict, fp8_path: Path) -> None:
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"""Load safetensors file into cache with LRU eviction."""
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file_path = fp8_path / file_name
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try:
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file_cache[file_name] = load_file(str(file_path), device=TORCH_DEVICE)
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file_cache.move_to_end(file_name)
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except Exception as e:
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logger.error(f"Failed to load {file_path}: {str(e)}")
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raise
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while len(file_cache) > CACHE_SIZE:
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oldest = next(iter(file_cache))
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del file_cache[oldest]
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torch.cuda.empty_cache()
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# Update model index
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new_model_index_file = os.path.join(bf16_path, "model.safetensors.index.json")
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def process_safetensor_file(
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file_path: Path,
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bf16_path: Path,
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weight_map: Dict[str, str],
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file_cache: OrderedDict,
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fp8_path: Path,
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fp8_weight_names: list
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) -> None:
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"""Process a single safetensors file."""
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try:
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current_state_dict = load_file(str(file_path), device=TORCH_DEVICE)
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file_cache[file_path.name] = current_state_dict
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new_state_dict = {}
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for weight_name, weight in tqdm(current_state_dict.items(),
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desc=f"Processing {file_path.name}",
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leave=False):
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processed_weight = process_weight(
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weight_name, weight, weight_map,
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file_cache, fp8_path, fp8_weight_names
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)
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if processed_weight is not None:
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new_state_dict[weight_name] = processed_weight
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save_converted_file(new_state_dict, file_path.name, bf16_path)
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except Exception as e:
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logger.error(f"Failed to process {file_path.name}: {str(e)}")
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raise
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def save_converted_file(state_dict: Dict[str, torch.Tensor], filename: str, bf16_path: Path) -> None:
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"""Save converted state dict to file."""
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output_path = bf16_path / filename
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try:
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save_file(state_dict, str(output_path), metadata={"converted": "fp8_to_bf16"})
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logger.debug(f"Saved converted file: {filename}")
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except Exception as e:
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logger.error(f"Failed to save {filename}: {str(e)}")
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raise
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def update_model_index(weight_map: Dict[str, str], fp8_weight_names: list, bf16_path: Path) -> None:
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"""Update model index file with converted weights."""
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for weight_name in fp8_weight_names:
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scale_inv_name = f"{weight_name}_scale_inv"
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if scale_inv_name in weight_map:
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weight_map.pop(scale_inv_name)
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with open(new_model_index_file, "w") as f:
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json.dump({"metadata": {}, "weight_map": weight_map}, f, indent=2)
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del weight_map[scale_inv_name]
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index_path = bf16_path / "model.safetensors.index.json"
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try:
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with open(index_path, "w") as f:
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json.dump({
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"metadata": {"conversion": "fp8_to_bf16"},
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"weight_map": weight_map
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}, f, indent=2)
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logger.info(f"Updated model index saved to {index_path}")
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except Exception as e:
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logger.error(f"Failed to save model index: {str(e)}")
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raise
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def main(fp8_path: Path, bf16_path: Path) -> None:
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"""Main conversion function."""
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torch.set_default_dtype(torch.bfloat16)
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validate_paths(fp8_path, bf16_path)
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try:
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model_index, weight_map = load_model_index(fp8_path)
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file_cache = OrderedDict()
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fp8_weight_names = []
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safetensor_files = sorted(fp8_path.glob("*.safetensors"))
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for safetensor_file in tqdm(safetensor_files, desc="Processing files"):
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process_safetensor_file(
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safetensor_file, bf16_path,
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weight_map, file_cache, fp8_path,
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fp8_weight_names
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)
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update_model_index(weight_map, fp8_weight_names, bf16_path)
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logger.info(f"Successfully converted {len(fp8_weight_names)} weights to BF16")
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except Exception as e:
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logger.error(f"Conversion failed: {str(e)}")
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raise
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if __name__ == "__main__":
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parser = ArgumentParser()
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parser.add_argument("--input-fp8-hf-path", type=str, required=True)
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parser.add_argument("--output-bf16-hf-path", type=str, required=True)
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args = parser.parse_args()
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main(args.input_fp8_hf_path, args.output_bf16_hf_path)
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parser = ArgumentParser(description="Convert FP8 model weights to BF16 format")
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parser.add_argument(
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"--input-fp8-hf-path",
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type=Path,
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required=True,
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help="Path to input directory with FP8 weights"
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)
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parser.add_argument(
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"--output-bf16-hf-path",
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type=Path,
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required=True,
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help="Output directory for converted BF16 weights"
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)
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args = parser.parse_args()
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try:
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main(args.input_fp8_hf_path, args.output_bf16_hf_path)
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except Exception as e:
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logger.critical(f"Fatal error during conversion: {str(e)}")
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exit(1)
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