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import argparse
import json
import os
import re
import struct
import sys
from pathlib import Path
from typing import Optional
from safetensors import safe_open
MODEL_SAFETENSORS_FILE = "model.safetensors"
MODEL_SAFETENSORS_INDEX = "model.safetensors.index.json"
DTYPE_SIZES = {
"F64": 8, "I64": 8, "U64": 8,
"F32": 4, "I32": 4, "U32": 4,
"F16": 2, "BF16": 2, "I16": 2, "U16": 2,
"I8": 1, "U8": 1, "BOOL": 1,
"F8_E4M3": 1, "F8_E5M2": 1,
}
SIZE_UNITS = ['B', 'KB', 'MB', 'GB', 'TB']
def get_weight_map(model_path: Path) -> Optional[dict[str, str]]:
index_file = model_path / MODEL_SAFETENSORS_INDEX
if index_file.exists():
with open(index_file, 'r') as f:
index = json.load(f)
return index.get("weight_map", {})
return None
def get_all_tensor_names(model_path: Path) -> list[str]:
weight_map = get_weight_map(model_path)
if weight_map is not None:
return list(weight_map.keys())
single_file = model_path / MODEL_SAFETENSORS_FILE
if single_file.exists():
try:
with safe_open(single_file, framework="pt", device="cpu") as f:
return list(f.keys())
except Exception as e:
print(f"Error reading {single_file}: {e}")
sys.exit(1)
print(f"Error: No safetensors files found in {model_path}")
sys.exit(1)
def find_tensor_file(model_path: Path, tensor_name: str) -> Optional[str]:
weight_map = get_weight_map(model_path)
if weight_map is not None:
return weight_map.get(tensor_name)
single_file = model_path / MODEL_SAFETENSORS_FILE
if single_file.exists():
return single_file.name
return None
def read_safetensors_header(file_path: Path) -> dict:
with open(file_path, 'rb') as f:
header_size = struct.unpack('<Q', f.read(8))[0]
return json.loads(f.read(header_size))
def get_tensor_size_bytes(tensor_meta: dict) -> int:
offsets = tensor_meta.get("data_offsets")
if offsets and len(offsets) == 2:
return offsets[1] - offsets[0]
n_elements = 1
for d in tensor_meta.get("shape", []):
n_elements *= d
return n_elements * DTYPE_SIZES.get(tensor_meta.get("dtype", "F32"), 4)
def format_size(size_bytes: int) -> str:
val = float(size_bytes)
for unit in SIZE_UNITS[:-1]:
if val < 1024.0:
return f"{val:.2f} {unit}"
val /= 1024.0
return f"{val:.2f} {SIZE_UNITS[-1]}"
def get_all_tensor_metadata(model_path: Path) -> dict[str, dict]:
weight_map = get_weight_map(model_path)
if weight_map is not None:
file_to_tensors: dict[str, list[str]] = {}
for tensor_name, file_name in weight_map.items():
file_to_tensors.setdefault(file_name, []).append(tensor_name)
all_metadata: dict[str, dict] = {}
for file_name, tensor_names in file_to_tensors.items():
try:
header = read_safetensors_header(model_path / file_name)
for tensor_name in tensor_names:
if tensor_name in header:
all_metadata[tensor_name] = header[tensor_name]
except Exception as e:
print(f"Warning: Could not read header from {file_name}: {e}", file=sys.stderr)
return all_metadata
single_file = model_path / MODEL_SAFETENSORS_FILE
if single_file.exists():
try:
header = read_safetensors_header(single_file)
return {k: v for k, v in header.items() if k != "__metadata__"}
except Exception as e:
print(f"Error reading {single_file}: {e}")
sys.exit(1)
print(f"Error: No safetensors files found in {model_path}")
sys.exit(1)
def normalize_tensor_name(tensor_name: str) -> str:
normalized = re.sub(r'\.\d+\.', '.#.', tensor_name)
normalized = re.sub(r'\.\d+$', '.#', normalized)
return normalized
def list_all_tensors(
model_path: Path,
short: bool = False,
show_sizes: bool = False,
):
tensor_names = get_all_tensor_names(model_path)
metadata: Optional[dict[str, dict]] = None
if show_sizes:
metadata = get_all_tensor_metadata(model_path)
total_bytes = 0
if short:
seen: dict[str, str] = {}
for tensor_name in sorted(tensor_names):
normalized = normalize_tensor_name(tensor_name)
if normalized not in seen:
seen[normalized] = tensor_name
display_pairs = list(sorted(seen.items()))
name_width = max((len(n) for n, _ in display_pairs), default=0)
for normalized, first_name in display_pairs:
if metadata and first_name in metadata:
m = metadata[first_name]
size = get_tensor_size_bytes(m)
total_bytes += size
print(f"{normalized:{name_width}} {m.get('dtype', '?'):6s} {str(m.get('shape', '')):30s} {format_size(size)}")
else:
print(normalized)
else:
name_width = max((len(n) for n in tensor_names), default=0)
for tensor_name in sorted(tensor_names):
if metadata and tensor_name in metadata:
m = metadata[tensor_name]
size = get_tensor_size_bytes(m)
total_bytes += size
print(f"{tensor_name:{name_width}} {m.get('dtype', '?'):6s} {str(m.get('shape', '')):30s} {format_size(size)}")
else:
print(tensor_name)
if show_sizes:
print(f"\nTotal: {format_size(total_bytes)}")
def print_tensor_info(model_path: Path, tensor_name: str, num_values: Optional[int] = None):
tensor_file = find_tensor_file(model_path, tensor_name)
if tensor_file is None:
print(f"Error: Could not find tensor '{tensor_name}' in model index")
print(f"Model path: {model_path}")
sys.exit(1)
file_path = model_path / tensor_file
try:
header = read_safetensors_header(file_path)
tensor_meta = header.get(tensor_name, {})
dtype_str = tensor_meta.get("dtype")
with safe_open(file_path, framework="pt", device="cpu") as f:
if tensor_name in f.keys():
tensor_slice = f.get_slice(tensor_name)
shape = tensor_slice.get_shape()
print(f"Tensor: {tensor_name}")
print(f"File: {tensor_file}")
print(f"Shape: {shape}")
if dtype_str:
print(f"Dtype: {dtype_str}")
if tensor_meta:
print(f"Size: {format_size(get_tensor_size_bytes(tensor_meta))}")
if num_values is not None:
tensor = f.get_tensor(tensor_name)
if not dtype_str:
print(f"Dtype: {tensor.dtype}")
flat = tensor.flatten()
n = min(num_values, flat.numel())
print(f"Values: {flat[:n].tolist()}")
else:
print(f"Error: Tensor '{tensor_name}' not found in {tensor_file}")
sys.exit(1)
except FileNotFoundError:
print(f"Error: The file '{file_path}' was not found.")
sys.exit(1)
except Exception as e:
print(f"An error occurred: {e}")
sys.exit(1)
def main():
parser = argparse.ArgumentParser(
description="Print tensor information from a safetensors model"
)
parser.add_argument(
"tensor_name",
nargs="?",
help="Name of the tensor to inspect"
)
parser.add_argument(
"-m", "--model-path",
type=Path,
help="Path to the model directory (default: MODEL_PATH environment variable)"
)
parser.add_argument(
"-l", "--list-all-short",
action="store_true",
help="List unique tensor patterns (layer numbers replaced with #)"
)
parser.add_argument(
"-la", "--list-all",
action="store_true",
help="List all tensor names with actual layer numbers"
)
parser.add_argument(
"-n", "--num-values",
nargs="?",
const=10,
default=None,
type=int,
metavar="N",
help="Print the first N values of the tensor flattened (default: 10 if flag is given without a number)"
)
parser.add_argument(
"-s", "--sizes",
action="store_true",
help="Show dtype, shape, and size for each tensor when listing"
)
args = parser.parse_args()
model_path = args.model_path
if model_path is None:
model_path_str = os.environ.get("MODEL_PATH")
if model_path_str is None:
print("Error: --model-path not provided and MODEL_PATH environment variable not set")
sys.exit(1)
model_path = Path(model_path_str)
if not model_path.exists():
print(f"Error: Model path does not exist: {model_path}")
sys.exit(1)
if not model_path.is_dir():
print(f"Error: Model path is not a directory: {model_path}")
sys.exit(1)
if args.list_all_short or args.list_all:
list_all_tensors(model_path, short=args.list_all_short, show_sizes=args.sizes)
else:
if args.tensor_name is None:
print("Error: tensor_name is required when not using --list-all-short or --list-all")
sys.exit(1)
print_tensor_info(model_path, args.tensor_name, args.num_values)
if __name__ == "__main__":
main()
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