290 lines
13 KiB
Python
Executable File
290 lines
13 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
from __future__ import annotations
|
|
|
|
import logging
|
|
import argparse
|
|
import os
|
|
import sys
|
|
from pathlib import Path
|
|
from typing import Any
|
|
|
|
import numpy as np
|
|
|
|
# Necessary to load the local gguf package
|
|
if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
|
|
sys.path.insert(0, str(Path(__file__).parent.parent))
|
|
|
|
from gguf import GGUFReader, GGUFValueType # noqa: E402
|
|
|
|
logger = logging.getLogger("gguf-dump")
|
|
|
|
|
|
def get_file_host_endian(reader: GGUFReader) -> tuple[str, str]:
|
|
host_endian = 'LITTLE' if np.uint32(1) == np.uint32(1).newbyteorder("<") else 'BIG'
|
|
if reader.byte_order == 'S':
|
|
file_endian = 'BIG' if host_endian == 'LITTLE' else 'LITTLE'
|
|
else:
|
|
file_endian = host_endian
|
|
return (host_endian, file_endian)
|
|
|
|
|
|
# For more information about what field.parts and field.data represent,
|
|
# please see the comments in the modify_gguf.py example.
|
|
def dump_metadata(reader: GGUFReader, args: argparse.Namespace) -> None:
|
|
host_endian, file_endian = get_file_host_endian(reader)
|
|
print(f'* File is {file_endian} endian, script is running on a {host_endian} endian host.') # noqa: NP100
|
|
print(f'* Dumping {len(reader.fields)} key/value pair(s)') # noqa: NP100
|
|
for n, field in enumerate(reader.fields.values(), 1):
|
|
if not field.types:
|
|
pretty_type = 'N/A'
|
|
elif field.types[0] == GGUFValueType.ARRAY:
|
|
nest_count = len(field.types) - 1
|
|
pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count
|
|
else:
|
|
pretty_type = str(field.types[-1].name)
|
|
|
|
log_message = f' {n:5}: {pretty_type:10} | {len(field.data):8} | {field.name}'
|
|
if len(field.types) == 1:
|
|
curr_type = field.types[0]
|
|
if curr_type == GGUFValueType.STRING:
|
|
log_message += ' = {0}'.format(repr(str(bytes(field.parts[-1]), encoding='utf-8')[:60]))
|
|
elif field.types[0] in reader.gguf_scalar_to_np:
|
|
log_message += ' = {0}'.format(field.parts[-1][0])
|
|
print(log_message) # noqa: NP100
|
|
if args.no_tensors:
|
|
return
|
|
print(f'* Dumping {len(reader.tensors)} tensor(s)') # noqa: NP100
|
|
for n, tensor in enumerate(reader.tensors, 1):
|
|
prettydims = ', '.join('{0:5}'.format(d) for d in list(tensor.shape) + [1] * (4 - len(tensor.shape)))
|
|
print(f' {n:5}: {tensor.n_elements:10} | {prettydims} | {tensor.tensor_type.name:7} | {tensor.name}') # noqa: NP100
|
|
|
|
|
|
def dump_metadata_json(reader: GGUFReader, args: argparse.Namespace) -> None:
|
|
import json
|
|
host_endian, file_endian = get_file_host_endian(reader)
|
|
metadata: dict[str, Any] = {}
|
|
tensors: dict[str, Any] = {}
|
|
result = {
|
|
"filename": args.model,
|
|
"endian": file_endian,
|
|
"metadata": metadata,
|
|
"tensors": tensors,
|
|
}
|
|
for idx, field in enumerate(reader.fields.values()):
|
|
curr: dict[str, Any] = {
|
|
"index": idx,
|
|
"type": field.types[0].name if field.types else 'UNKNOWN',
|
|
"offset": field.offset,
|
|
}
|
|
metadata[field.name] = curr
|
|
if field.types[:1] == [GGUFValueType.ARRAY]:
|
|
curr["array_types"] = [t.name for t in field.types][1:]
|
|
if not args.json_array:
|
|
continue
|
|
itype = field.types[-1]
|
|
if itype == GGUFValueType.STRING:
|
|
curr["value"] = [str(bytes(field.parts[idx]), encoding="utf-8") for idx in field.data]
|
|
else:
|
|
curr["value"] = [pv for idx in field.data for pv in field.parts[idx].tolist()]
|
|
elif field.types[0] == GGUFValueType.STRING:
|
|
curr["value"] = str(bytes(field.parts[-1]), encoding="utf-8")
|
|
else:
|
|
curr["value"] = field.parts[-1].tolist()[0]
|
|
if not args.no_tensors:
|
|
for idx, tensor in enumerate(reader.tensors):
|
|
tensors[tensor.name] = {
|
|
"index": idx,
|
|
"shape": tensor.shape.tolist(),
|
|
"type": tensor.tensor_type.name,
|
|
"offset": tensor.field.offset,
|
|
}
|
|
json.dump(result, sys.stdout)
|
|
|
|
|
|
def element_count_rounded_notation(count: int) -> str:
|
|
if count > 1e15 :
|
|
# Quadrillion
|
|
scaled_amount = count * 1e-15
|
|
scale_suffix = "Q"
|
|
elif count > 1e12 :
|
|
# Trillions
|
|
scaled_amount = count * 1e-12
|
|
scale_suffix = "T"
|
|
elif count > 1e9 :
|
|
# Billions
|
|
scaled_amount = count * 1e-9
|
|
scale_suffix = "B"
|
|
elif count > 1e6 :
|
|
# Millions
|
|
scaled_amount = count * 1e-6
|
|
scale_suffix = "M"
|
|
elif count > 1e3 :
|
|
# Thousands
|
|
scaled_amount = count * 1e-3
|
|
scale_suffix = "K"
|
|
else:
|
|
# Under Thousands
|
|
scaled_amount = count
|
|
scale_suffix = ""
|
|
return f"{'~' if count > 1e3 else ''}{round(scaled_amount)}{scale_suffix}"
|
|
|
|
|
|
def translate_tensor_name(name):
|
|
words = name.split(".")
|
|
|
|
# Source: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#standardized-tensor-names
|
|
abbreviation_dictionary = {
|
|
'token_embd': 'Token embedding',
|
|
'pos_embd': 'Position embedding',
|
|
'output_norm': 'Output normalization',
|
|
'output': 'Output',
|
|
'attn_norm': 'Attention normalization',
|
|
'attn_norm_2': 'Attention normalization',
|
|
'attn_qkv': 'Attention query-key-value',
|
|
'attn_q': 'Attention query',
|
|
'attn_k': 'Attention key',
|
|
'attn_v': 'Attention value',
|
|
'attn_output': 'Attention output',
|
|
'ffn_norm': 'Feed-forward network normalization',
|
|
'ffn_up': 'Feed-forward network "up"',
|
|
'ffn_gate': 'Feed-forward network "gate"',
|
|
'ffn_down': 'Feed-forward network "down"',
|
|
'ffn_gate_inp': 'Expert-routing layer for the Feed-forward network in Mixture of Expert models',
|
|
'ffn_gate_exp': 'Feed-forward network "gate" layer per expert in Mixture of Expert models',
|
|
'ffn_down_exp': 'Feed-forward network "down" layer per expert in Mixture of Expert models',
|
|
'ffn_up_exp': 'Feed-forward network "up" layer per expert in Mixture of Expert models',
|
|
'ssm_in': 'State space model input projections',
|
|
'ssm_conv1d': 'State space model rolling/shift',
|
|
'ssm_x': 'State space model selective parametrization',
|
|
'ssm_a': 'State space model state compression',
|
|
'ssm_d': 'State space model skip connection',
|
|
'ssm_dt': 'State space model time step',
|
|
'ssm_out': 'State space model output projection',
|
|
'blk': 'Block'
|
|
}
|
|
|
|
expanded_words = []
|
|
for word in words:
|
|
word_norm = word.strip().lower()
|
|
if word_norm in abbreviation_dictionary:
|
|
expanded_words.append(abbreviation_dictionary[word_norm].title())
|
|
else:
|
|
expanded_words.append(word.title())
|
|
|
|
return ' '.join(expanded_words)
|
|
|
|
|
|
def dump_markdown_metadata(reader: GGUFReader, args: argparse.Namespace) -> None:
|
|
host_endian, file_endian = get_file_host_endian(reader)
|
|
markdown_content = ""
|
|
markdown_content += f'# {args.model} - GGUF Internal File Dump\n'
|
|
markdown_content += f'* Endian: {file_endian} endian\n'
|
|
markdown_content += '\n'
|
|
markdown_content += '## Key Value Metadata Store\n'
|
|
markdown_content += f'There is {len(reader.fields)} key/value pair(s) in this file\n'
|
|
markdown_content += '\n'
|
|
|
|
markdown_content += '| POS | TYPE | Elements | Key | Value |\n'
|
|
markdown_content += '|-----|------------|----------|----------------------------------------|--------------------------------------------------------------------------------|\n'
|
|
|
|
for n, field in enumerate(reader.fields.values(), 1):
|
|
if not field.types:
|
|
pretty_type = 'N/A'
|
|
elif field.types[0] == GGUFValueType.ARRAY:
|
|
nest_count = len(field.types) - 1
|
|
pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count
|
|
else:
|
|
pretty_type = str(field.types[-1].name)
|
|
|
|
if len(field.types) == 1:
|
|
curr_type = field.types[0]
|
|
if curr_type == GGUFValueType.STRING:
|
|
value = repr(str(bytes(field.parts[-1]), encoding='utf-8')[:60])
|
|
elif field.types[0] in reader.gguf_scalar_to_np:
|
|
value = field.parts[-1][0]
|
|
markdown_content += f'| {n:3} | {pretty_type:10} | {len(field.data):8} | {field.name:38} | {value:<78} |\n'
|
|
|
|
markdown_content += "\n"
|
|
|
|
if not args.no_tensors:
|
|
# Group tensors by their prefix and maintain order
|
|
tensor_prefix_order = []
|
|
tensor_name_to_key = {}
|
|
tensor_groups = {}
|
|
total_elements = sum(tensor.n_elements for tensor in reader.tensors)
|
|
|
|
for key, tensor in enumerate(reader.tensors):
|
|
tensor_components = tensor.name.split('.')
|
|
tensor_prefix = tensor_components[0]
|
|
|
|
if tensor_prefix == 'blk':
|
|
tensor_prefix = f"{tensor_components[0]}.{tensor_components[1]}"
|
|
|
|
if tensor_prefix not in tensor_groups:
|
|
tensor_groups[tensor_prefix] = []
|
|
tensor_prefix_order.append(tensor_prefix)
|
|
|
|
tensor_name_to_key[tensor.name] = key
|
|
tensor_groups[tensor_prefix].append(tensor)
|
|
|
|
# Tensors Mapping Dump
|
|
markdown_content += f'## Tensors Overview {element_count_rounded_notation(total_elements)} Elements\n'
|
|
markdown_content += f'Total number of elements in all tensors: {total_elements} Elements\n'
|
|
markdown_content += '\n'
|
|
|
|
for group in tensor_prefix_order:
|
|
tensors = tensor_groups[group]
|
|
group_elements = sum(tensor.n_elements for tensor in tensors)
|
|
markdown_content += f"- [{translate_tensor_name(group)} Tensor Group - {element_count_rounded_notation(group_elements)} Elements](#{group.replace('.', '_')})\n"
|
|
|
|
markdown_content += "\n"
|
|
|
|
for group in tensor_prefix_order:
|
|
tensors = tensor_groups[group]
|
|
group_elements = sum(tensor.n_elements for tensor in tensors)
|
|
group_percentage = group_elements / total_elements * 100
|
|
markdown_content += f"### {translate_tensor_name(group)} Tensor Group : {element_count_rounded_notation(group_elements)} Elements <a name=\"{group.replace('.', '_')}\"></a>\n"
|
|
markdown_content += "| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |\n"
|
|
markdown_content += "|------|---------------------------|----------------------------------------------------|----------------|---------------------------------|------|\n"
|
|
|
|
for tensor in tensors:
|
|
human_friendly_name = translate_tensor_name(tensor.name.replace(".weight", ".(W)").replace(".bias", ".(B)"))
|
|
prettydims = ' x '.join('{0:^5}'.format(d) for d in list(tensor.shape) + [1] * (4 - len(tensor.shape)))
|
|
markdown_content += f"| {tensor_name_to_key[tensor.name]:4} | {tensor.name:25} | {human_friendly_name:50} | ({element_count_rounded_notation(tensor.n_elements):>4}) {tensor.n_elements:7} | [{prettydims:29}] | {tensor.tensor_type.name:4} |\n"
|
|
markdown_content += "\n"
|
|
markdown_content += f"- Total elements in {group}: ({element_count_rounded_notation(group_elements):>4}) {group_elements}\n"
|
|
markdown_content += f"- Percentage of total elements: {group_percentage:.2f}%\n"
|
|
markdown_content += "\n\n"
|
|
|
|
print(markdown_content) # noqa: NP100
|
|
|
|
|
|
def main() -> None:
|
|
parser = argparse.ArgumentParser(description="Dump GGUF file metadata")
|
|
parser.add_argument("model", type=str, help="GGUF format model filename")
|
|
parser.add_argument("--no-tensors", action="store_true", help="Don't dump tensor metadata")
|
|
parser.add_argument("--json", action="store_true", help="Produce JSON output")
|
|
parser.add_argument("--json-array", action="store_true", help="Include full array values in JSON output (long)")
|
|
parser.add_argument("--markdown", action="store_true", help="Produce markdown output")
|
|
parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
|
|
|
|
args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
|
|
|
|
logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
|
|
|
|
if not args.json and not args.markdown:
|
|
logger.info(f'* Loading: {args.model}')
|
|
|
|
reader = GGUFReader(args.model, 'r')
|
|
|
|
if args.json:
|
|
dump_metadata_json(reader, args)
|
|
elif args.markdown:
|
|
dump_markdown_metadata(reader, args)
|
|
else:
|
|
dump_metadata(reader, args)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|