From 80d0407b4c250a61fab0c3afb0e4fd62de319141 Mon Sep 17 00:00:00 2001 From: Martijn van Beers Date: Thu, 5 Dec 2024 15:39:34 +0100 Subject: [PATCH 1/3] Add pyproject.toml copied from the tarball on pypi so I can (dev-)install my fork --- pyproject.toml | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 pyproject.toml diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..571ebc2 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,28 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "calflops" +version = "0.3.2" +authors = [ + { name="MrYXJ", email="yxj2017@gmail.com"}, +] +description = "This package is designed to compute the theoretical amount of FLOPs(floating-point operations)、MACs(multiply-add operations) and Parameters in all various neural networks, such as Linear、 CNN、 RNN、 GCN、Transformer(Bert、LlaMA etc Large Language Model)，including any custom models via torch.nn.function.* as long as based on the Pytorch implementation." + +readme = "README.md" +license = { file="LICENSE" } +requires-python = ">=3.6" +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", +] +dependencies = [ + "torch>=1.1.0", + "accelerate>=0.22.0", + "huggingface_hub>=0.16.4" +] + +[project.urls] +"Homepage" = "https://github.com/MrYxJ/calculate-flops.pytorch" \ No newline at end of file From 6db399199f8c8c2d5404a35128f4f9e784145d43 Mon Sep 17 00:00:00 2001 From: Martijn van Beers Date: Thu, 5 Dec 2024 15:54:39 +0100 Subject: [PATCH 2/3] Remove __pycache__ Remove the compiled python files, and add __pycache__ to the .gitignore so they won't come back --- .gitignore | 1 + calflops/__pycache__/__init__.cpython-311.pyc | Bin 1020 -> 0 bytes calflops/__pycache__/__init__.cpython-39.pyc | Bin 850 -> 0 bytes .../__pycache__/big_modeling.cpython-39.pyc | Bin 4690 -> 0 bytes .../calculate_pipline.cpython-311.pyc | Bin 17281 -> 0 bytes .../calculate_pipline.cpython-39.pyc | Bin 13298 -> 0 bytes calflops/__pycache__/estimate.cpython-311.pyc | Bin 10534 -> 0 bytes calflops/__pycache__/estimate.cpython-39.pyc | Bin 6258 -> 0 bytes calflops/__pycache__/estimate.py | 271 ------------------ .../__pycache__/flops_counter.cpython-311.pyc | Bin 9750 -> 0 bytes .../__pycache__/flops_counter.cpython-39.pyc | Bin 7376 -> 0 bytes .../flops_counter_hf.cpython-311.pyc | Bin 7228 -> 0 bytes .../flops_counter_hf.cpython-39.pyc | Bin 5444 -> 0 bytes .../__pycache__/pytorch_ops.cpython-311.pyc | Bin 32714 -> 0 bytes .../__pycache__/pytorch_ops.cpython-39.pyc | Bin 14879 -> 0 bytes calflops/__pycache__/utils.cpython-311.pyc | Bin 10076 -> 0 bytes calflops/__pycache__/utils.cpython-39.pyc | Bin 6696 -> 0 bytes 17 files changed, 1 insertion(+), 271 deletions(-) create mode 100644 .gitignore delete mode 100644 calflops/__pycache__/__init__.cpython-311.pyc delete mode 100644 calflops/__pycache__/__init__.cpython-39.pyc delete mode 100644 calflops/__pycache__/big_modeling.cpython-39.pyc delete mode 100644 calflops/__pycache__/calculate_pipline.cpython-311.pyc delete mode 100644 calflops/__pycache__/calculate_pipline.cpython-39.pyc delete mode 100644 calflops/__pycache__/estimate.cpython-311.pyc delete mode 100644 calflops/__pycache__/estimate.cpython-39.pyc delete mode 100644 calflops/__pycache__/estimate.py delete mode 100644 calflops/__pycache__/flops_counter.cpython-311.pyc delete mode 100644 calflops/__pycache__/flops_counter.cpython-39.pyc delete mode 100644 calflops/__pycache__/flops_counter_hf.cpython-311.pyc delete mode 100644 calflops/__pycache__/flops_counter_hf.cpython-39.pyc delete mode 100644 calflops/__pycache__/pytorch_ops.cpython-311.pyc delete mode 100644 calflops/__pycache__/pytorch_ops.cpython-39.pyc delete mode 100644 calflops/__pycache__/utils.cpython-311.pyc delete mode 100644 calflops/__pycache__/utils.cpython-39.pyc diff --git 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HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -#import argparse - -from huggingface_hub import model_info -from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError - -from accelerate import init_empty_weights -from accelerate.utils import ( - # calculate_maximum_sizes, - # convert_bytes, - is_timm_available, - is_transformers_available, -) - - -if is_transformers_available(): - import transformers - from transformers import AutoConfig, AutoModel - -if is_timm_available(): - import timm - - -def verify_on_hub(repo: str, token: str = None): - "Verifies that the model is on the hub and returns the model info." - try: - return model_info(repo, token=token) - except GatedRepoError: - return "gated" - except RepositoryNotFoundError: - return "repo" - - -def check_has_model(error): - """ - Checks what library spawned `error` when a model is not found - """ - if is_timm_available() and isinstance(error, RuntimeError) and "Unknown model" in error.args[0]: - return "timm" - elif ( - is_transformers_available() - and isinstance(error, OSError) - and "does not appear to have a file named" in error.args[0] - ): - return "transformers" - else: - return "unknown" - - -def create_empty_model(model_name: str, library_name: str, trust_remote_code: bool = False, access_token: str = None): - """ - Creates an empty model from its parent library on the `Hub` to calculate the overall memory consumption. - - Args: - model_name (`str`): - The model name on the Hub - library_name (`str`): - The library the model has an integration with, such as `transformers`. Will be used if `model_name` has no - metadata on the Hub to determine the library. - trust_remote_code (`bool`, `optional`, defaults to `False`): - Whether or not to allow for custom models defined on the Hub in their own modeling files. This option - should only be set to `True` for repositories you trust and in which you have read the code, as it will - execute code present on the Hub on your local machine. - access_token (`str`, `optional`, defaults to `None`): - The access token to use to access private or gated models on the Hub. (for use on the Gradio app) - - Returns: - `torch.nn.Module`: The torch model that has been initialized on the `meta` device. - - """ - model_info = verify_on_hub(model_name, access_token) - # Simplified errors - if model_info == "gated": - raise GatedRepoError( - f"Repo for model `{model_name}` is gated. You must be authenticated to access it. Please run `huggingface-cli login`." - ) - elif model_info == "repo": - raise RepositoryNotFoundError( - f"Repo for model `{model_name}` does not exist on the Hub. If you are trying to access a private repo," - " make sure you are authenticated via `huggingface-cli login` and have access." - ) - if library_name is None: - library_name = getattr(model_info, "library_name", False) - if not library_name: - raise ValueError( - f"Model `{model_name}` does not have any library metadata on the Hub, please manually pass in a `--library_name` to use (such as `transformers`)" - ) - if library_name == "transformers": - if not is_transformers_available(): - raise ImportError( - f"To check `{model_name}`, `transformers` must be installed. Please install it via `pip install transformers`" - ) - print(f"Loading pretrained config for `{model_name}` from `transformers`...") - - #auto_map = model_info.config.get("auto_map", False) - auto_map = model_info.config.get("auto_map", True) - config = AutoConfig.from_pretrained(model_name, trust_remote_code=trust_remote_code) - - with init_empty_weights(): - # remote code could specify a specific `AutoModel` class in the `auto_map` - constructor = AutoModel - if isinstance(auto_map, dict): - value = None - for key in auto_map.keys(): - if key.startswith("AutoModelFor"): - value = key - break - if value is not None: - constructor = getattr(transformers, value) - model = constructor.from_config(config, trust_remote_code=trust_remote_code) - elif library_name == "timm": - if not is_timm_available(): - raise ImportError( - f"To check `{model_name}`, `timm` must be installed. Please install it via `pip install timm`" - ) - print(f"Loading pretrained config for `{model_name}` from `timm`...") - with init_empty_weights(): - model = timm.create_model(model_name, pretrained=False) - else: - raise ValueError( - f"Library `{library_name}` is not supported yet, please open an issue on GitHub for us to add support." - ) - return model - - -def create_ascii_table(headers: list, rows: list, title: str): - "Creates a pretty table from a list of rows, minimal version of `tabulate`." - sep_char, in_between = "│", "─" - column_widths = [] - for i in range(len(headers)): - column_values = [row[i] for row in rows] + [headers[i]] - max_column_width = max(len(value) for value in column_values) - column_widths.append(max_column_width) - - formats = [f"%{column_widths[i]}s" for i in range(len(rows[0]))] - - pattern = f"{sep_char}{sep_char.join(formats)}{sep_char}" - diff = 0 - - def make_row(left_char, middle_char, right_char): - return f"{left_char}{middle_char.join([in_between * n for n in column_widths])}{in_between * diff}{right_char}" - - separator = make_row("├", "┼", "┤") - if len(title) > sum(column_widths): - diff = abs(len(title) - len(separator)) - column_widths[-1] += diff - - # Update with diff - separator = make_row("├", "┼", "┤") - initial_rows = [ - make_row("┌", in_between, "┐"), - f"{sep_char}{title.center(len(separator) - 2)}{sep_char}", - make_row("├", "┬", "┤"), - ] - table = "\n".join(initial_rows) + "\n" - column_widths[-1] += diff - centered_line = [text.center(column_widths[i]) for i, text in enumerate(headers)] - table += f"{pattern % tuple(centered_line)}\n{separator}\n" - for i, line in enumerate(rows): - centered_line = [t.center(column_widths[i]) for i, t in enumerate(line)] - table += f"{pattern % tuple(centered_line)}\n" - table += f'└{"┴".join([in_between * n for n in column_widths])}┘' - - return table - - -# def estimate_command_parser(subparsers=None): -# if subparsers is not None: -# parser = subparsers.add_parser("estimate-memory") -# else: -# parser = argparse.ArgumentParser(description="Model size estimator for fitting a model onto CUDA memory.") - -# parser.add_argument("model_name", type=str, help="The model name on the Hugging Face Hub.") -# parser.add_argument( -# "--library_name", -# type=str, -# help="The library the model has an integration with, such as `transformers`, needed only if this information is not stored on the Hub.", -# choices=["timm", "transformers"], -# ) -# parser.add_argument( -# "--dtypes", -# type=str, -# nargs="+", -# default=["float32", "float16", "int8", "int4"], -# help="The dtypes to use for the model, must be one (or many) of `float32`, `float16`, `int8`, and `int4`", -# choices=["float32", "float16", "int8", "int4"], -# ) -# parser.add_argument( -# "--trust_remote_code", -# action="store_true", -# help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. This flag -# should only be used for repositories you trust and in which you have read the code, as it will execute -# code present on the Hub on your local machine.""", -# ) - -# if subparsers is not None: -# parser.set_defaults(func=estimate_command) -# return parser - - -# def gather_data(args): -# "Creates an empty model and gathers the data for the sizes" -# try: -# model = create_empty_model( -# args.model_name, library_name=args.library_name, trust_remote_code=args.trust_remote_code -# ) -# except (RuntimeError, OSError) as e: -# library = check_has_model(e) -# if library != "unknown": -# raise RuntimeError( -# f"Tried to load `{args.model_name}` with `{library}` but a possible model to load was not found inside the repo." -# ) -# raise e - -# total_size, largest_layer = calculate_maximum_sizes(model) - -# data = [] - -# for dtype in args.dtypes: -# dtype_total_size = total_size -# dtype_largest_layer = largest_layer[0] -# if dtype == "float16": -# dtype_total_size /= 2 -# dtype_largest_layer /= 2 -# elif dtype == "int8": -# dtype_total_size /= 4 -# dtype_largest_layer /= 4 -# elif dtype == "int4": -# dtype_total_size /= 8 -# dtype_largest_layer /= 8 -# dtype_training_size = dtype_total_size * 4 -# data.append([dtype, dtype_largest_layer, dtype_total_size, dtype_training_size]) -# return data - - -# def estimate_command(args): -# data = gather_data(args) -# for row in data: -# for i, item in enumerate(row): -# if isinstance(item, (int, float)): -# row[i] = convert_bytes(item) - -# headers = ["dtype", "Largest Layer", "Total Size", "Training using Adam"] - -# title = f"Memory Usage for loading `{args.model_name}`" -# table = create_ascii_table(headers, data, title) -# print(table) - - -# def main(): -# parser = estimate_command_parser() -# args = parser.parse_args() -# estimate_command(args) - - -# if __name__ == "__main__": -# main() \ No newline at end of file diff --git a/calflops/__pycache__/flops_counter.cpython-311.pyc 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through model.apply(). They are run right before the print function. This lets you do things like store the FLOPS for each module rather than just printing them, as the new example script shows. --- calflops/flops_counter.py | 6 ++++ calflops/flops_counter_hf.py | 6 ++++ test_examples/test_extra_func.py | 47 ++++++++++++++++++++++++++++++++ 3 files changed, 59 insertions(+) create mode 100644 test_examples/test_extra_func.py diff --git a/calflops/flops_counter.py b/calflops/flops_counter.py index ba216f3..4a2c6a2 100644 --- a/calflops/flops_counter.py +++ b/calflops/flops_counter.py @@ -30,6 +30,7 @@ def calculate_flops(model, forward_mode="forward", include_backPropagation=False, compute_bp_factor=2.0, + extra_apply_funcs=[], print_results=True, print_detailed=True, output_as_string=True, @@ -48,6 +49,7 @@ def calculate_flops(model, forward_mode (str, optional): To determine the mode of model inference, Default to 'forward'. And use 'generate' if model inference uses model.generate(). include_backPropagation (bool, optional): Decides whether the final return FLOPs computation includes the computation for backpropagation. compute_bp_factor (float, optional): The model backpropagation is a multiple of the forward propagation computation. Default to 2. + extra_apply_funcs (bool, optional): A list of functions to use in model.apply() which will be run before print_results. Defaults to the empty list. print_results (bool, optional): Whether to print the model profile. Defaults to True. print_detailed (bool, optional): Whether to print the detailed model profile. Defaults to True. output_as_string (bool, optional): Whether to print the output as string. Defaults to True. @@ -172,6 +174,10 @@ def calculate_flops(model, macs = calculate_flops_pipline.get_total_macs() params = calculate_flops_pipline.get_total_params() + if len(extra_apply_funcs) > 0: + for func in extra_apply_funcs: + model.apply(func) + if print_results: return_print = calculate_flops_pipline.print_model_pipline(units=output_unit, precision=output_precision, diff --git a/calflops/flops_counter_hf.py b/calflops/flops_counter_hf.py index 5d4e21e..edfc42e 100644 --- a/calflops/flops_counter_hf.py +++ b/calflops/flops_counter_hf.py @@ -33,6 +33,7 @@ def calculate_flops_hf(model_name, forward_mode="forward", include_backPropagation=False, compute_bp_factor=2.0, + extra_apply_funcs=[], print_results=True, print_detailed=True, output_as_string=True, @@ -51,6 +52,7 @@ def calculate_flops_hf(model_name, forward_mode (str, optional): To determine the mode of model inference, Default to 'forward'. And use 'generate' if model inference uses model.generate(). include_backPropagation (bool, optional): Decides whether the final return FLOPs computation includes the computation for backpropagation. compute_bp_factor (float, optional): The model backpropagation is a multiple of the forward propagation computation. Default to 2. + extra_apply_funcs (bool, optional): A list of functions to use in model.apply() which will be run before print_results. Defaults to the empty list. print_results (bool, optional): Whether to print the model profile. Defaults to True. print_detailed (bool, optional): Whether to print the detailed model profile. Defaults to True. output_as_string (bool, optional): Whether to print the output as string. Defaults to True. @@ -126,6 +128,10 @@ def calculate_flops_hf(model_name, params = calculate_flops_pipline.get_total_params() + if len(extra_apply_funcs) > 0: + for func in extra_apply_funcs: + model.apply(func) + print_return = calculate_flops_pipline.print_return_model_pipline(units=output_unit, precision=output_precision, print_detailed=print_detailed, diff --git a/test_examples/test_extra_func.py b/test_examples/test_extra_func.py new file mode 100644 index 0000000..6d383c2 --- /dev/null +++ b/test_examples/test_extra_func.py @@ -0,0 +1,47 @@ +# !usr/bin/env python +# -*- coding:utf-8 -*- + +''' + Description : + Version : 1.0 + Author : MrYXJ + Mail : yxj2017@gmail.com + Github : https://github.com/MrYxJ + Date : 2023-08-24 11:48:59 + LastEditTime : 2023-08-24 19:42:16 + Copyright (C) 2023 mryxj. All rights reserved. +''' + +from calflops import calculate_flops +from calflops.utils import get_module_flops +from transformers import AutoModel +from transformers import AutoTokenizer +from collections import deque +from functools import partial + +def store_flops(module, datastore=None): + data = dict() + children = list(module.named_children()) + if len(children) > 0: + datastore.rotate(len(children)) + for name, child in children: + typename, flops, child_data = datastore.popleft() + data[name] = (typename, flops, child_data,) + datastore.append((type(module).__name__, get_module_flops(module), data,)) + +myqueue = deque() + +batch_size = 1 +max_seq_length = 128 +model_name = "bert-base-uncased" + +model = AutoModel.from_pretrained(model_name) +tokenizer = AutoTokenizer.from_pretrained(model_name) + +flops, macs, params = calculate_flops(model=model, + extra_apply_funcs=[partial(store_flops, datastore=myqueue)], + print_detailed=False, + input_shape=(batch_size,max_seq_length), + transformer_tokenizer=tokenizer) +print("Bert(hfl/chinese-roberta-wwm-ext) FLOPs:%s MACs:%s Params:%s \n" %(flops, macs, params)) +print(myqueue)