

'''simple docstring'''







# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
#     ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
#     --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
#     --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
#    1. --tf32 0 --fp16 0
#    2. --tf32 0 --fp16 1
#    3. --tf32 0 --bf16 1
#    4. --tf32 1 --fp16 0
#    5. --tf32 1 --fp16 1
#    6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
#    --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
#    --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
#    1. --tf32 0
#    2. --tf32 0 --fp16
#    3. --tf32 0 --bf16
#    4. --tf32 1
#    5. --tf32 1 --fp16
#    6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
#    `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation       |     Train |   Diff |   Train |
# |                 |   samples |      % |    loss |
# |                 |       per |        |         |
# |                 |    second |        |         |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0        |    285.11 |      0 |    2.51 |
# | --tf32 1        |    342.09 |     20 |    2.51 |
# | --fp16 --tf32 0 |    423.49 |     49 |    2.51 |
# | --fp16 --tf32 1 |    423.13 |     48 |    2.51 |
# | --bf16 --tf32 0 |    416.80 |     46 |    2.52 |
# | --bf16 --tf32 1 |    415.87 |     46 |    2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
#    --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#

import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean

import pandas as pd
import torch
from tqdm import tqdm

import transformers


__lowerCamelCase									=     float('''nan''')


class        A__   :



    def __init__(       self		,					UpperCamelCase__							)    ->   int:





           '''simple docstring'''
           A_						       =   sys.stdout
           A_						       =   open(UpperCamelCase__		,					"""a"""							)



    def __getattr__(       self		,					UpperCamelCase__							)    ->   Tuple:





           '''simple docstring'''
           return getattr(self.stdout		,					UpperCamelCase__							)







    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   Any:





           '''simple docstring'''
           self.stdout.write(UpperCamelCase__							)
           # strip tqdm codes
           self.file.write(re.sub(R"""^.*\r"""		,					""""""		,					UpperCamelCase__		,					0		,					re.M							)							)

def    UpperCAmelCase__     (   UpperCAmelCase__=80,							UpperCAmelCase__=False					)      ->      Union[str, Any]:
       A_						       =   []

       # deal with critical env vars
       A_						       =   ["""CUDA_VISIBLE_DEVICES"""]
       for key in env_keys:
              A_						       =   os.environ.get(UpperCAmelCase__,							UpperCAmelCase__					)
              if val is not None:
                     cmd.append(F'''{key}={val}'''					)

    # python executable (not always needed if the script is executable)
       A_						       =   sys.executable if full_python_path else sys.executable.split("""/"""					)[-1]
       cmd.append(UpperCAmelCase__					)

       # now the normal args
       cmd += list(map(shlex.quote,							sys.argv					)					)

       # split up into up to MAX_WIDTH lines with shell multi-line escapes
       A_						       =   []
       A_						       =   """"""
       while len(UpperCAmelCase__					) > 0:
              current_line += F'''{cmd.pop(0					)} '''
              if len(UpperCAmelCase__					) == 0 or len(UpperCAmelCase__					) + len(cmd[0]					) + 1 > max_width - 1:
                     lines.append(UpperCAmelCase__					)
                     A_						       =   """"""
       return "\\\n".join(UpperCAmelCase__					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      Any:
       # unwrap multi-line input
       A_						       =   re.sub(r"""[\\\n]+""",							""" """,							args.base_cmd					)

       # remove --output_dir if any and set our own
       A_						       =   re.sub("""--output_dir\s+[^\s]+""",							"""""",							args.base_cmd					)
       args.base_cmd += F''' --output_dir {output_dir}'''

       # ensure we have --overwrite_output_dir
       A_						       =   re.sub("""--overwrite_output_dir\s+""",							"""""",							args.base_cmd					)
       args.base_cmd += " --overwrite_output_dir"

       return [sys.executable] + shlex.split(args.base_cmd					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      List[str]:
       # Enable to debug everything but the run itself, to do it fast and see the progress.
       # This is useful for debugging the output formatting quickly - we can remove it later once
       # everybody is happy with the output
       if 0:
              import random
              from time import sleep

              sleep(0					)
              return dict(
                  {k: random.uniform(0,							1_00					) for k in metric_keys},							**{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6_666, 222.22_222_222]					)},							)

       A_						       =   subprocess.run(UpperCAmelCase__,							capture_output=UpperCAmelCase__,							text=UpperCAmelCase__					)

       if verbose:
              print("""STDOUT""",							result.stdout					)
              print("""STDERR""",							result.stderr					)

       # save the streams
       A_						       =   variation.replace(""" """,							"""-"""					)
       with open(Path(UpperCAmelCase__					) / F'''log.{prefix}.stdout.txt''',							"""w"""					) as f:
              f.write(result.stdout					)
       with open(Path(UpperCAmelCase__					) / F'''log.{prefix}.stderr.txt''',							"""w"""					) as f:
              f.write(result.stderr					)

       if result.returncode != 0:
              if verbose:
                     print("""failed"""					)
              return {target_metric_key: nan}

       with io.open(F'''{output_dir}/all_results.json''',							"""r""",							encoding="""utf-8"""					) as f:
              A_						       =   json.load(UpperCAmelCase__					)

       # filter out just the keys we want
       return {k: v for k, v in metrics.items() if k in metric_keys}
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							)      ->      str:
       A_						       =   []
       A_						       =   []
       A_						       =   F'''{id}: {variation:<{longest_variation_len}}'''
       A_						       =   F'''{preamble}: '''
       A_						       =   set(report_metric_keys + [target_metric_key]					)
       for i in tqdm(range(UpperCAmelCase__					),							desc=UpperCAmelCase__,							leave=UpperCAmelCase__					):
              A_						       =   process_run_single(
                  UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)
              A_						       =   single_run_metrics[target_metric_key]
              if not math.isnan(UpperCAmelCase__					):
                     metrics.append(UpperCAmelCase__					)
                     results.append(UpperCAmelCase__					)
                     outcome += "✓"
              else:
                     outcome += "✘"
       A_						       =   F'''\33[2K\r{outcome}'''
       if len(UpperCAmelCase__					) > 0:
              A_						       =   {k: fmean([x[k] for x in metrics]					) for k in metrics[0].keys()}
              A_						       =   round(mean_metrics[target_metric_key],							2					)
              A_						       =   F'''{outcome} {mean_target}'''
              if len(UpperCAmelCase__					) > 1:
                     results_str += F''' {tuple(round(UpperCAmelCase__,							2					) for x in results					)}'''
              print(UpperCAmelCase__					)
              A_						       =   variation
              return mean_metrics
       else:
              print(UpperCAmelCase__					)
              return {variation_key: variation, target_metric_key: nan}
def    UpperCAmelCase__     (   )      ->      Any:
       A_						       =   torch.cuda.get_device_properties(torch.device("""cuda"""					)					)
       return F'''
Datetime    : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"					)}

Software:
transformers: {transformers.__version__}
torch       : {torch.__version__}
cuda        : {torch.version.cuda}
python      : {platform.python_version()}

Hardware:
{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**30:0.2f}GB
'''
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      str:
       A_						       =   pd.DataFrame(UpperCAmelCase__					)
       A_						       =   """variation"""
       A_						       =   """diff_%"""

       A_						       =   nan
       if base_variation is not None and len(df[df[variation_key] == base_variation]					):
              # this may still return nan
              A_						       =   df.loc[df[variation_key] == base_variation][target_metric_key].item()
       if math.isnan(UpperCAmelCase__					):
              # as a fallback, use the minimal value as the sentinel
              A_						       =   df.loc[df[target_metric_key] != nan][target_metric_key].min()

       # create diff column if possible
       if not math.isnan(UpperCAmelCase__					):
              A_						       =   df.apply(
                  lambda UpperCAmelCase__					: round(1_00 * (r[target_metric_key] - sentinel_value) / sentinel_value					)
                  if not math.isnan(r[target_metric_key]					)
                  else 0,							axis="""columns""",							)

       # re-order columns
       A_						       =   [variation_key, target_metric_key, diff_key, *report_metric_keys]
       A_						       =   df.reindex(UpperCAmelCase__,							axis="""columns"""					)  # reorder cols

       # capitalize
       A_						       =   df.rename(str.capitalize,							axis="""columns"""					)

       # make the cols as narrow as possible
       A_						       =   df.rename(lambda UpperCAmelCase__					: c.replace("""_""",							"""<br>"""					),							axis="""columns"""					)
       A_						       =   df.rename(lambda UpperCAmelCase__					: c.replace("""_""",							"""\n"""					),							axis="""columns"""					)

       A_						       =   ["""""", """Copy between the cut-here-lines and paste as is to github or a forum"""]
       report += ["----------8<-----------------8<--------"]
       report += ["*** Results:", df_github.to_markdown(index=UpperCAmelCase__,							floatfmt=""".2f"""					)]
       report += ["```"]
       report += ["*** Setup:", get_versions()]
       report += ["*** The benchmark command line was:", get_original_command()]
       report += ["```"]
       report += ["----------8<-----------------8<--------"]
       report += ["*** Results (console):", df_console.to_markdown(index=UpperCAmelCase__,							floatfmt=""".2f"""					)]

       print("""\n\n""".join(UpperCAmelCase__					)					)
def    UpperCAmelCase__     (   )      ->      Union[str, Any]:
       A_						       =   argparse.ArgumentParser()
       parser.add_argument(
           """--base-cmd""",							default=UpperCAmelCase__,							type=UpperCAmelCase__,							required=UpperCAmelCase__,							help="""Base cmd""",							)
       parser.add_argument(
           """--variations""",							default=UpperCAmelCase__,							type=UpperCAmelCase__,							nargs="""+""",							required=UpperCAmelCase__,							help="""Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'""",							)
       parser.add_argument(
           """--base-variation""",							default=UpperCAmelCase__,							type=UpperCAmelCase__,							help="""Baseline variation to compare to. if None the minimal target value will be used to compare against""",							)
       parser.add_argument(
           """--target-metric-key""",							default=UpperCAmelCase__,							type=UpperCAmelCase__,							required=UpperCAmelCase__,							help="""Target metric key in output_dir/all_results.json, e.g., train_samples_per_second""",							)
       parser.add_argument(
           """--report-metric-keys""",							default="""""",							type=UpperCAmelCase__,							help="""Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., 'train_loss train_samples""",							)
       parser.add_argument(
           """--repeat-times""",							default=1,							type=UpperCAmelCase__,							help="""How many times to re-run each variation - an average will be reported""",							)
       parser.add_argument(
           """--output_dir""",							default="""output_benchmark""",							type=UpperCAmelCase__,							help="""The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked""",							)
       parser.add_argument(
           """--verbose""",							default=UpperCAmelCase__,							action="""store_true""",							help="""Whether to show the outputs of each run or just the benchmark progress""",							)
       A_						       =   parser.parse_args()

       A_						       =   args.output_dir
       Path(UpperCAmelCase__					).mkdir(exist_ok=UpperCAmelCase__					)
       A_						       =   get_base_command(UpperCAmelCase__,							UpperCAmelCase__					)

       # split each dimension into its --foo variations
       A_						       =   [list(map(str.strip,							re.split(r"""\|""",							UpperCAmelCase__					)					)					) for x in args.variations]
       # build a cartesian product of dimensions and convert those back into cmd-line arg strings,
       # while stripping white space for inputs that were empty
       A_						       =   list(map(str.strip,							map(""" """.join,							itertools.product(*UpperCAmelCase__					)					)					)					)
       A_						       =   max(len(UpperCAmelCase__					) for x in variations					)

       # split wanted keys
       A_						       =   args.report_metric_keys.split()

       # capture prints into a log file for convenience
       A_						       =   F'''benchmark-report-{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S"					)}.txt'''
       print(F'''\nNote: each run\'s output is also logged under {output_dir}/log.*.std*.txt'''					)
       print(F'''and this script\'s output is also piped into {report_fn}'''					)

       A_						       =   Tee(UpperCAmelCase__					)

       print(F'''\n*** Running {len(UpperCAmelCase__					)} benchmarks:'''					)
       print(F'''Base command: {" ".join(UpperCAmelCase__					)}'''					)

       A_						       =   """variation"""
       A_						       =   []
       for id, variation in enumerate(tqdm(UpperCAmelCase__,							desc="""Total completion: """,							leave=UpperCAmelCase__					)					):
              A_						       =   base_cmd + variation.split()
              results.append(
                  process_run(
                      id + 1,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							args.target_metric_key,							UpperCAmelCase__,							args.repeat_times,							UpperCAmelCase__,							args.verbose,							)					)

       process_results(UpperCAmelCase__,							args.target_metric_key,							UpperCAmelCase__,							args.base_variation,							UpperCAmelCase__					)


if __name__ == "__main__":
      main()



'''simple docstring'''







from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__lowerCamelCase									=     logging.get_logger(__name__)


__lowerCamelCase									=     {
    '''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
    '''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
    '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
    '''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}


class        A__   (   _snake_case      ):
    lowercase						       =		"codegen"
    lowercase						       =		{
        "max_position_embeddings": "n_positions",
        "hidden_size": "n_embd",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }



    def __init__(       self		,					UpperCamelCase__=50400		,					UpperCamelCase__=2048		,					UpperCamelCase__=2048		,					UpperCamelCase__=4096		,					UpperCamelCase__=28		,					UpperCamelCase__=16		,					UpperCamelCase__=64		,					UpperCamelCase__=None		,					UpperCamelCase__="gelu_new"		,					UpperCamelCase__=0.0		,					UpperCamelCase__=0.0		,					UpperCamelCase__=0.0		,					UpperCamelCase__=1e-5		,					UpperCamelCase__=0.02		,					UpperCamelCase__=True		,					UpperCamelCase__=50256		,					UpperCamelCase__=50256		,					UpperCamelCase__=False		,					**UpperCamelCase__		,					)    ->   Tuple:





           '''simple docstring'''
           A_						       =   vocab_size
           A_						       =   n_ctx
           A_						       =   n_positions
           A_						       =   n_embd
           A_						       =   n_layer
           A_						       =   n_head
           A_						       =   n_inner
           A_						       =   rotary_dim
           A_						       =   activation_function
           A_						       =   resid_pdrop
           A_						       =   embd_pdrop
           A_						       =   attn_pdrop
           A_						       =   layer_norm_epsilon
           A_						       =   initializer_range
           A_						       =   use_cache

           A_						       =   bos_token_id
           A_						       =   eos_token_id

           super().__init__(
               bos_token_id=UpperCamelCase__		,					eos_token_id=UpperCamelCase__		,					tie_word_embeddings=UpperCamelCase__		,					**UpperCamelCase__							)


class        A__   (   _snake_case      ):



    def __init__(       self		,					UpperCamelCase__		,					UpperCamelCase__ = "default"		,					UpperCamelCase__ = None		,					UpperCamelCase__ = False		,					)    ->   Tuple:





           '''simple docstring'''
           super().__init__(UpperCamelCase__		,					task=UpperCamelCase__		,					patching_specs=UpperCamelCase__		,					use_past=UpperCamelCase__							)
           if not getattr(self._config		,					"""pad_token_id"""		,					UpperCamelCase__							):
                  # TODO: how to do that better?
                  A_						       =   0



    @property
    def        snake_case_      (       self							)    ->   Mapping[str, Mapping[int, str]]:





           '''simple docstring'''
           A_						       =   OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}							)
           if self.use_past:
                  self.fill_with_past_key_values_(UpperCamelCase__		,					direction="""inputs"""							)
                  A_						       =   {0: """batch""", 1: """past_sequence + sequence"""}
           else:
                  A_						       =   {0: """batch""", 1: """sequence"""}

           return common_inputs



    @property
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           return self._config.n_layer



    @property
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           return self._config.n_head



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__ = -1		,					UpperCamelCase__ = -1		,					UpperCamelCase__ = False		,					UpperCamelCase__ = None		,					)    ->   Mapping[str, Any]:





           '''simple docstring'''
           A_						       =   super(UpperCamelCase__		,					self							).generate_dummy_inputs(
               UpperCamelCase__		,					batch_size=UpperCamelCase__		,					seq_length=UpperCamelCase__		,					is_pair=UpperCamelCase__		,					framework=UpperCamelCase__							)

           # We need to order the input in the way they appears in the forward()
           A_						       =   OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}							)

           # Need to add the past_keys
           if self.use_past:
                  if not is_torch_available():
                         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
                  else:
                         import torch

                         A_       ,      A_						       =   common_inputs["""input_ids"""].shape
                         # Not using the same length for past_key_values
                         A_						       =   seqlen + 2
                         A_						       =   (
                             batch,
                             self.num_attention_heads,
                             past_key_values_length,
                             self._config.hidden_size // self.num_attention_heads,
                         )
                         A_						       =   [
                             (torch.zeros(UpperCamelCase__							), torch.zeros(UpperCamelCase__							)) for _ in range(self.num_layers							)
                         ]

           A_						       =   common_inputs["""attention_mask"""]
           if self.use_past:
                  A_						       =   ordered_inputs["""attention_mask"""].dtype
                  A_						       =   torch.cat(
                      [ordered_inputs["""attention_mask"""], torch.ones(UpperCamelCase__		,					UpperCamelCase__		,					dtype=UpperCamelCase__							)]		,					dim=1							)

           return ordered_inputs







    @property
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           return 13
