safetensors_info.py

import argparse
from safetensors import safe_open
from collections import Counter
import os
import math # math.prod is Python 3.8+

# --- Dtype to Bytes Mapping ---
# Safetensors Dtype strings:
# BOOL, F8_E5M2, F8_E4M3FN, F16, BF16, F32, F64,
# I8, I16, I32, I64, U8, U16, U32, U64,
# F8_E5M2FNUZ, F8_E4M3FNUZ
DTYPE_TO_BYTES = {
    "BOOL": 1,
    # Float8 variants
    "F8_E5M2": 1,
    "F8E5M2": 1,        # Common alternative naming
    "F8_E4M3FN": 1,
    "F8E4M3FN": 1,      # Common alternative naming
    "F8_E4M3": 1,       # As seen in user example, likely E4M3FN
    "F8_E5M2FNUZ": 1,
    "F8E5M2FNUZ": 1,    # Common alternative naming
    "F8_E4M3FNUZ": 1,
    "F8E4M3FNUZ": 1,    # Common alternative naming
    # Standard floats
    "F16": 2,
    "BF16": 2,
    "F32": 4,
    "F64": 8,
    # Integers
    "I8": 1,
    "I16": 2,
    "I32": 4,
    "I64": 8,
    # Unsigned Integers
    "U8": 1,
    "U16": 2,
    "U32": 4,
    "U64": 8,
}

def get_bytes_per_element(dtype_str):
    """Returns the number of bytes for a given safetensors dtype string."""
    return DTYPE_TO_BYTES.get(dtype_str.upper(), None)

def calculate_num_elements(shape):
    """Calculates the total number of elements from a tensor shape tuple."""
    if not shape:  # Scalar tensor (shape is ())
        return 1
    if 0 in shape: # If any dimension is 0, total elements is 0
        return 0
    # Using math.prod for conciseness if Python 3.8+
    # For broader compatibility, a loop can be used:
    num_elements = 1
    for dim_size in shape:
        num_elements *= dim_size
    return num_elements

def inspect_safetensors_precision_and_size(filepath):
    """
    Reads a .safetensors file, iterates through its tensors,
    and reports the precision (dtype), actual size, and theoretical FP32 size.
    """
    if not os.path.exists(filepath):
        print(f"Error: File not found at '{filepath}'")
        return

    if not filepath.lower().endswith(".safetensors"):
        print(f"Warning: File '{filepath}' does not have a .safetensors extension. Attempting to read anyway.")

    tensor_info_list = []
    dtype_counts = Counter()
    total_actual_mb = 0.0
    total_fp32_equiv_mb = 0.0

    try:
        print(f"Inspecting tensors in: {filepath}\n")
        with safe_open(filepath, framework="pt", device="cpu") as f:
            tensor_keys = list(f.keys())
            if not tensor_keys:
                print("No tensors found in the file.")
                return

            max_key_len = len("Tensor Name") # Default/minimum
            if tensor_keys:
                 max_key_len = max(max_key_len, max(len(k) for k in tensor_keys))

            header = (
                f"{'Tensor Name':<{max_key_len}} | "
                f"{'Precision (dtype)':<17} | "
                f"{'Actual Size (MB)':>16} | "
                f"{'FP32 Equiv. (MB)':>18}"
            )
            print(header)
            print(
                f"{'-' * max_key_len}-|-------------------|------------------|-------------------"
            )

            for key in tensor_keys:
                tensor_slice = f.get_slice(key)
                dtype_str = tensor_slice.get_dtype()
                shape = tensor_slice.get_shape()

                num_elements = calculate_num_elements(shape)
                bytes_per_el_actual = get_bytes_per_element(dtype_str)

                actual_size_mb_str = "N/A"
                fp32_equiv_size_mb_str = "N/A"
                actual_size_mb_val = 0.0

                if bytes_per_el_actual is not None:
                    actual_bytes = num_elements * bytes_per_el_actual
                    actual_size_mb_val = actual_bytes / (1024 * 1024)
                    total_actual_mb += actual_size_mb_val
                    actual_size_mb_str = f"{actual_size_mb_val:.3f}"

                    # Theoretical FP32 size (FP32 is 4 bytes per element)
                    fp32_equiv_bytes = num_elements * 4
                    fp32_equiv_size_mb_val = fp32_equiv_bytes / (1024 * 1024)
                    total_fp32_equiv_mb += fp32_equiv_size_mb_val
                    fp32_equiv_size_mb_str = f"{fp32_equiv_size_mb_val:.3f}"
                else:
                    print(f"Warning: Unknown dtype '{dtype_str}' for tensor '{key}'. Cannot calculate size.")

                print(
                    f"{key:<{max_key_len}} | "
                    f"{dtype_str:<17} | "
                    f"{actual_size_mb_str:>16} | "
                    f"{fp32_equiv_size_mb_str:>18}"
                )
                dtype_counts[dtype_str] += 1

        print("\n--- Summary ---")
        print(f"Total tensors found: {len(tensor_keys)}")
        if dtype_counts:
            print("Precision distribution:")
            for dtype, count in dtype_counts.most_common():
                print(f"  - {dtype:<12}: {count} tensor(s)")
        else:
            print("No dtypes to summarize.")

        print(f"\nTotal actual size of all tensors: {total_actual_mb:.3f} MB")
        print(f"Total theoretical FP32 size of all tensors: {total_fp32_equiv_mb:.3f} MB")

        if total_fp32_equiv_mb > 0.00001: # Avoid division by zero or near-zero
            savings_percentage = (1 - (total_actual_mb / total_fp32_equiv_mb)) * 100
            print(f"Overall size reduction compared to full FP32: {savings_percentage:.2f}%")
        else:
            print("Overall size reduction cannot be calculated (no FP32 equivalent data or zero size).")

    except Exception as e:
        print(f"An error occurred while processing '{filepath}':")
        print(f"  {e}")
        print("Please ensure it's a valid .safetensors file and the 'safetensors' (and 'torch') libraries are installed correctly.")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Inspect tensor precision (dtype) and size in a .safetensors file."
    )
    parser.add_argument(
        "filepath",
        help="Path to the .safetensors file to inspect."
    )
    args = parser.parse_args()

    inspect_safetensors_precision_and_size(args.filepath)