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gguf-dump.py: prettyfy dimention

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brian khuu 2024-06-15 00:05:04 +10:00
parent 0b181a9df5
commit 9310a02a7b
1 changed files with 18 additions and 7 deletions
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25
gguf-py/scripts/gguf-dump.py
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@ -101,7 +101,7 @@ def dump_metadata_json(reader: GGUFReader, args: argparse.Namespace) -> None:
json.dump(result, sys.stdout) json.dump(result, sys.stdout)
def markdownTableWithAlignmentSupport(header_map: list[dict[str, str]], data: list[dict[str, Any]]): def markdown_table_with_alignment_support(header_map: list[dict[str, str]], data: list[dict[str, Any]]):
# JSON to Markdown table formatting: https://stackoverflow.com/a/72983854/2850957 # JSON to Markdown table formatting: https://stackoverflow.com/a/72983854/2850957
# Alignment Utility Function # Alignment Utility Function
@ -272,7 +272,7 @@ def dump_markdown_metadata(reader: GGUFReader, args: argparse.Namespace) -> None
{'key_name':'value', 'header_name':'Value', 'align':'left'}, {'key_name':'value', 'header_name':'Value', 'align':'left'},
] ]
markdown_content += markdownTableWithAlignmentSupport(kv_dump_table_header_map, kv_dump_table) markdown_content += markdown_table_with_alignment_support(kv_dump_table_header_map, kv_dump_table)
markdown_content += "\n" markdown_content += "\n"
@ -319,24 +319,35 @@ def dump_markdown_metadata(reader: GGUFReader, args: argparse.Namespace) -> None
group_percentage = group_elements / total_elements * 100 group_percentage = group_elements / total_elements * 100
markdown_content += f"### <a name=\"{group.replace('.', '_')}\">{translate_tensor_name(group)} Tensor Group : {element_count_rounded_notation(group_elements)} Elements</a>\n\n" markdown_content += f"### <a name=\"{group.replace('.', '_')}\">{translate_tensor_name(group)} Tensor Group : {element_count_rounded_notation(group_elements)} Elements</a>\n\n"
# Precalculate pretty shape column sizing for visual consistency
prettify_dimension_max_widths = {}
for tensor in tensors:
for i, dimension_size in enumerate(list(tensor.shape) + [1] * (4 - len(tensor.shape))):
if i in prettify_dimension_max_widths:
prettify_dimension_max_widths[i] = max(prettify_dimension_max_widths[i], len(str(dimension_size)))
else:
prettify_dimension_max_widths[i] = len(str(dimension_size))
# Generate Tensor Layer Table Content
tensor_dump_table = [] tensor_dump_table = []
for tensor in tensors: for tensor in tensors:
human_friendly_name = translate_tensor_name(tensor.name.replace(".weight", ".(W)").replace(".bias", ".(B)")) 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))) pretty_dimension = ' x '.join(f'{str(d):^{prettify_dimension_max_widths[i]}}' for i, d in enumerate(list(tensor.shape) + [1] * (4 - len(tensor.shape))))
element_count_string = f"({element_count_rounded_notation(tensor.n_elements):>4}) {tensor.n_elements:7}" element_count_est = f"({element_count_rounded_notation(tensor.n_elements):>4})"
element_count_string = f"{element_count_est:>6} {tensor.n_elements:^8}"
type_name_string = f"{tensor.tensor_type.name}" type_name_string = f"{tensor.tensor_type.name}"
tensor_dump_table.append({"t_id":tensor_name_to_key[tensor.name], "layer_name":tensor.name, "human_layer_name":human_friendly_name, "element_count":element_count_string, "pretty_dims":prettydims, "tensor_type":type_name_string}) tensor_dump_table.append({"t_id":tensor_name_to_key[tensor.name], "layer_name":tensor.name, "human_layer_name":human_friendly_name, "element_count":element_count_string, "pretty_dimension":pretty_dimension, "tensor_type":type_name_string})
tensor_dump_table_header_map = [ tensor_dump_table_header_map = [
{'key_name':'t_id', 'header_name':'T_ID', 'align':'right'}, {'key_name':'t_id', 'header_name':'T_ID', 'align':'right'},
{'key_name':'layer_name', 'header_name':'Tensor Layer Name', 'align':'left'}, {'key_name':'layer_name', 'header_name':'Tensor Layer Name', 'align':'left'},
{'key_name':'human_layer_name', 'header_name':'Human Friendly Tensor Layer Name', 'align':'left'}, {'key_name':'human_layer_name', 'header_name':'Human Friendly Tensor Layer Name', 'align':'left'},
{'key_name':'element_count', 'header_name':'Elements', 'align':'left'}, {'key_name':'element_count', 'header_name':'Elements', 'align':'left'},
{'key_name':'pretty_dims', 'header_name':'Shape', 'align':'left'}, {'key_name':'pretty_dimension', 'header_name':'Shape', 'align':'left'},
{'key_name':'tensor_type', 'header_name':'Type', 'align':'left'}, {'key_name':'tensor_type', 'header_name':'Type', 'align':'left'},
] ]
markdown_content += markdownTableWithAlignmentSupport(tensor_dump_table_header_map, tensor_dump_table) markdown_content += markdown_table_with_alignment_support(tensor_dump_table_header_map, tensor_dump_table)
markdown_content += "\n" markdown_content += "\n"
markdown_content += f"- Total elements in {group}: ({element_count_rounded_notation(group_elements):>4}) {group_elements}\n" markdown_content += f"- Total elements in {group}: ({element_count_rounded_notation(group_elements):>4}) {group_elements}\n"