pytorch weights added

.gitattributes

@@ -15,3 +15,4 @@
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pytorch_model.bin filter=lfs diff=lfs merge=lfs -text

config.json

@@ -1,4 +1,5 @@
 {
+  "_name_or_path": "./",
   "activation_function": "gelu_new",
   "architectures": [
     "GPT2LMHeadModel"
@@ -30,6 +31,7 @@
       "max_length": 50
     }
   },
+  "torch_dtype": "float32",
   "transformers_version": "4.9.0.dev0",
   "use_cache": true,
   "vocab_size": 50257

jax2tensor.py

@@ -0,0 +1,21 @@
+import torch
+import numpy as np
+import jax
+import jax.numpy as jnp
+from transformers import AutoTokenizer
+from transformers import FlaxGPT2LMHeadModel
+from transformers import GPT2LMHeadModel
+
+
+model_fx = FlaxGPT2LMHeadModel.from_pretrained("./")
+model_pt = GPT2LMHeadModel.from_pretrained("./", from_flax=True)
+
+model_pt.save_pretrained("./")
+
+
+input_ids = np.asarray(2 * [128 * [0]], dtype=np.int32)
+input_ids_pt = torch.tensor(input_ids)
+logits_pt = model_pt(input_ids_pt).logits
+print(logits_pt)
+logits_fx = model_fx(input_ids).logits
+print(logits_fx)

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6739f146a875869da1ed7f1fd23bb877f6be6e6d27a58ed84cfe626773d62aa1
+size 510401385

run_2.sh

@@ -1,22 +0,0 @@
-#!/usr/bin/env bash
-python run_clm_flax_v2.py \
-    --output_dir="${MODEL_DIR}" \
-    --model_type="gpt2" \
-    --config_name="${MODEL_DIR}" \
-    --tokenizer_name="${MODEL_DIR}" \
-    --dataset_name="mc4" \
-    --dataset_config_name="bn" \
-    --do_train --do_eval \
-    --block_size="512" \
-    --per_device_train_batch_size="64" \
-    --per_device_eval_batch_size="64" \
-    --learning_rate="5e-3" --warmup_steps="1000" \
-    --adam_beta1="0.9" --adam_beta2="0.98" --weight_decay="0.01" \
-    --overwrite_output_dir \
-    --max_steps="100000" \
-    --decay_steps="100000" \
-    --logging_steps="50" \
-    --save_steps="50" \
-    --eval_steps="50" \
-    --max_eval_samples 100 \
-    --push_to_hub

run_clm_flax_v2.py

@@ -1,823 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2021 The 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.
-"""
-Pre-training/Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
-Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
-https://huggingface.co/models?filter=causal-lm
-"""
-# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
-
-from ast import Str
-import logging
-import math
-import os
-import sys
-import time
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Callable, Optional
-import json
-import shutil
-from collections import defaultdict
-from flax import training
-import numpy as np
-import datasets
-from datasets import Dataset, load_dataset
-from tqdm import tqdm
-
-import jax
-import jax.profiler
-import jax.numpy as jnp
-import optax
-import transformers
-from flax import jax_utils, traverse_util
-from flax.jax_utils import unreplicate
-from flax.training import train_state
-from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
-from flax.training.checkpoints import save_checkpoint, restore_checkpoint
-from flax.serialization import to_bytes, from_bytes
-from transformers import (
-    CONFIG_MAPPING,
-    FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
-    AutoConfig,
-    AutoTokenizer,
-    FlaxAutoModelForCausalLM,
-    HfArgumentParser,
-    TrainingArguments,
-    is_tensorboard_available,
-)
-from transformers.testing_utils import CaptureLogger
-
-from importlib.util import find_spec
-from utils import PrefetchDataloader, make_batch
-
-logger = logging.getLogger(__name__)
-
-MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_CAUSAL_LM_MAPPING.keys())
-MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
-
-
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
-    """
-
-    model_name_or_path: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "The model checkpoint for weights initialization."
-            "Don't set if you want to train a model from scratch."
-        },
-    )
-    model_type: Optional[str] = field(
-        default=None,
-        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
-    )
-    config_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
-    )
-    tokenizer_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
-    )
-    cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
-    )
-    use_fast_tokenizer: bool = field(
-        default=True,
-        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
-    )
-    dtype: Optional[str] = field(
-        default="float32",
-        metadata={
-            "help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
-        },
-    )
-    save_optimizer: Optional[bool] = field(
-        default=True,
-        metadata={"help": "Whether to store full train state including optimizer."},
-    )
-    repo_path_or_name: Optional[str] = field(
-        default=None,
-        metadata={"help": "Path to the modelhub repo directory"},
-    )
-    repo_url: Optional[str] = field(
-        default=None,
-        metadata={"help": "URL of the modelhub repo"},
-    )
-    decay_steps: int = field(default=None, metadata={"help":"Number of steps from peak to final learning rate"})
-
-@dataclass
-class DataTrainingArguments:
-    """
-    Arguments pertaining to what data we are going to input our model for training and eval.
-    """
-
-    dataset_name: Optional[str] = field(
-        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
-    )
-    dataset_config_name: Optional[str] = field(
-        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
-    )
-    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
-    validation_file: Optional[str] = field(
-        default=None,
-        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
-    )
-    data_dir: Optional[str] = field(default=None, metadata={"help": "Path to data directory."})
-    max_train_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
-            "value if set."
-        },
-    )
-    max_eval_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
-            "value if set."
-        },
-    )
-    overwrite_cache: bool = field(
-        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
-    )
-    validation_split_percentage: Optional[int] = field(
-        default=5,
-        metadata={
-            "help": "The percentage of the train set used as validation set in case there's no validation split"
-        },
-    )
-    block_size: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "Optional input sequence length after tokenization. "
-            "The training dataset will be truncated in block of this size for training. "
-            "Default to the model max input length for single sentence inputs (take into account special tokens)."
-        },
-    )
-    overwrite_cache: bool = field(
-        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
-    )
-    preprocessing_num_workers: Optional[int] = field(
-        default=None,
-        metadata={"help": "The number of processes to use for the preprocessing."},
-    )
-    text_column_name: Optional[str] = field(
-            default='text',
-            metadata={"help": "Column containing main text data."},
-        )
-    shuffle_buffer_size: int = field(
-        default=10000, metadata={"help": "The number of examples to pre-load for shuffling."}
-    )
-    num_train_steps: int = field(default=50000, metadata={"help": "The number of training steps."})
-    num_eval_samples: int = field(default=50000, metadata={"help": "The number of samples to be used for evaluation"})
-    prefetch_buffer: int = field(default=8, metadata={"help": "The number of batches to prefetch for loading"})
-
-    def __post_init__(self):
-        if self.dataset_name is None and self.train_file is None and self.validation_file is None:
-            raise ValueError("Need either a dataset name or a training/validation file.")
-        else:
-            if self.train_file is not None:
-                extension = self.train_file.split(".")[-1]
-                assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
-            if self.validation_file is not None:
-                extension = self.validation_file.split(".")[-1]
-                assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
-
-
-class TrainState(train_state.TrainState):
-    dropout_rng: jnp.ndarray
-
-    def replicate(self):
-        return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
-
-# the below functions are not used now, probably to be removed
-def generate_batch_splits(samples_idx: jnp.ndarray, batch_size: int) -> jnp.ndarray:
-    num_samples = len(samples_idx)
-    samples_to_remove = num_samples % batch_size
-
-    if samples_to_remove != 0:
-        samples_idx = samples_idx[:-samples_to_remove]
-    sections_split = num_samples // batch_size
-    batch_idx = np.split(samples_idx, sections_split)
-    return batch_idx
-
-
-def advance_iter_and_group_samples(train_iterator, num_samples, max_seq_length):
-    """
-    The training iterator is advanced so that after groupifying the samples,
-    `num_samples` of length `max_seq_length` are returned.
-    """
-    num_total_tokens = max_seq_length * num_samples
-    samples = defaultdict(list)
-
-    i = 0
-    while i < num_total_tokens:
-        tokenized_samples = next(train_iterator)
-        i += len(tokenized_samples["input_ids"])
-
-        # concatenate tokenized samples to list
-        samples = {k: samples[k] + tokenized_samples[k] for k in tokenized_samples.keys()}
-
-    # Concatenated tokens are split to lists of length `max_seq_length`.
-    # Note that remainedr of % max_seq_length are thrown away.
-    def group_texts(examples):
-        result = {
-            k: [t[i : i + max_seq_length] for i in range(0, num_total_tokens, max_seq_length)]
-            for k, t in examples.items()
-        }
-        return result
-
-    grouped_samples = group_texts(samples)
-    return grouped_samples
-
-def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False):
-    """
-    Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices.
-    Shuffle batches if `shuffle` is `True`.
-    """
-    steps_per_epoch = len(dataset) // batch_size
-
-    if shuffle:
-        batch_idx = jax.random.permutation(rng, len(dataset))
-    else:
-        batch_idx = jnp.arange(len(dataset))
-
-    batch_idx = batch_idx[: steps_per_epoch * batch_size]  # Skip incomplete batch.
-    batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
-
-    for idx in batch_idx:
-        batch = dataset[idx]
-        batch = {k: jnp.array(v) for k, v in batch.items()}
-
-        batch = shard(batch)
-
-        yield batch
-
-
-def write_train_metric(summary_writer, train_metrics, train_time, step):
-    summary_writer.scalar("train_time", train_time, step)
-
-    train_metrics = get_metrics(train_metrics)
-    for key, vals in train_metrics.items():
-        tag = f"train_{key}"
-        for i, val in enumerate(vals):
-            summary_writer.scalar(tag, val, step - len(vals) + i + 1)
-
-
-def write_eval_metric(summary_writer, eval_metrics, step):
-    for metric_name, value in eval_metrics.items():
-        summary_writer.scalar(f"eval_{metric_name}", value, step)
-
-
-def create_learning_rate_fn(
-    num_train_steps: int, train_batch_size: int, num_warmup_steps: int, learning_rate: float
-) -> Callable[[int], jnp.array]:
-    """Returns a linear warmup, linear_decay learning rate function."""
-    warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
-    decay_fn = optax.linear_schedule(
-        init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
-    )
-    schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
-    return schedule_fn
-def gpt3_schedule(warmup_steps,
-                  total_steps,
-                  peak_lr,
-                  end_lr):
-    def sch(step):
-        warmup_pct = jnp.clip(step, 0, warmup_steps) / warmup_steps
-        anneal_pct = jnp.clip(step - warmup_steps, 0, total_steps) / total_steps
-
-        return warmup_pct * peak_lr - (peak_lr - end_lr) * (1 - jnp.cos(jnp.pi * anneal_pct)) / 2
-
-    return sch
-
-# utils
-def mb_item(x):
-    return x.item() if hasattr(x, "item") else x
-
-#checkpoint functions
-def save_model_checkpoint(model, save_dir, state, with_opt=True, push_to_hub=False):
-    """
-    If `push_to_hub` is True, will save to `save_dir`. Otherwise will save to `save_dir/ckpt-{step}`.
-    """
-    state = jax_utils.unreplicate(state)
-    logger.info(f"SAVING CHECKPOINT IN {save_dir}...")
-    if not push_to_hub:
-        save_dir = f"{save_dir}/ckpt-{mb_item(state.step)-1}"
-    model.save_pretrained(
-        save_dir,
-        params=state.params,
-        push_to_hub=push_to_hub,
-        commit_message=f"Saving weights and logs at step {mb_item(state.step)-1}",
-    )
-    if with_opt:
-        with open(os.path.join(save_dir, "opt_state.msgpack"), "wb") as f:
-            f.write(to_bytes(state.opt_state))
-        with open(os.path.join(save_dir, "training_state.json"), "w") as f:
-            json.dump({"step": state.step.item()}, f)
-    logger.info("checkpoint saved")
-        
-def restore_model_checkpoint(save_dir, state):
-    logger.info(f"RESTORING CHECKPOINT FROM {save_dir}...")
-    with open(os.path.join(save_dir, "flax_model.msgpack"), "rb") as f:
-        params = from_bytes(state.params, f.read())
-
-    with open(os.path.join(save_dir, "opt_state.msgpack"), "rb") as f:
-        opt_state = from_bytes(state.opt_state, f.read())
-
-    with open(os.path.join(save_dir, "training_state.json"), "r") as f:
-        training_state = json.load(f)
-    step = training_state["step"]
-
-    logger.info("checkpoint restored")
-    return state.replace(step=step, params=params, opt_state=opt_state), step
-
-def rotate_checkpoints(ckpt_dir:str, save_total_limit:int):
-    "Removes older checkpoints so that `save_total_limit` checkpoints are kept"
-    # TODO: what to remove is decided using step number only, we might want to improve that
-    ckpts = [str(x) for x in Path(ckpt_dir).glob("ckpt-*")]
-    # sort checkpoints by step
-    ckpts_sorted = sorted(ckpts, key=lambda x: int(x.split('-')[-1]))
-    ckpts_to_delete = ckpts_sorted[:-save_total_limit]
-    for ckpt in ckpts_to_delete:
-        logger.info(f"Deleting older checkpoint [{ckpt}] due to save_total_limit ({save_total_limit})")
-        shutil.rmtree(ckpt)
-
-def main():
-    # See all possible arguments in src/transformers/training_args.py
-    # or by passing the --help flag to this script.
-    # We now keep distinct sets of args, for a cleaner separation of concerns.
-
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
-    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
-        # If we pass only one argument to the script and it's the path to a json file,
-        # let's parse it to get our arguments.
-        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
-    else:
-        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
-    if (
-        os.path.exists(training_args.output_dir)
-        and os.listdir(training_args.output_dir)
-        and training_args.do_train
-        and not training_args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty."
-            "Use --overwrite_output_dir to overcome."
-        )
-
-    # Make one log on every process with the configuration for debugging.
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    # Setup logging, we only want one process per machine to log things on the screen.
-    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
-    if jax.process_index() == 0:
-        datasets.utils.logging.set_verbosity_warning()
-        transformers.utils.logging.set_verbosity_info()
-    else:
-        datasets.utils.logging.set_verbosity_error()
-        transformers.utils.logging.set_verbosity_error()
-
-    # Set the verbosity to info of the Transformers logger (on main process only):
-    logger.info(f"Training/evaluation parameters {training_args}")
-
-    #  Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
-    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
-    # (the dataset will be downloaded automatically from the datasets Hub).
-    #
-    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
-    # 'text' is found. You can easily tweak this behavior (see below).
-    #
-    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
-    # download the dataset.
-    if data_args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        train_dataset = load_dataset(
-            data_args.dataset_name,
-            data_args.dataset_config_name,
-            cache_dir=model_args.cache_dir, 
-            streaming=True,
-            split="train"
-        )
-        eval_dataset = load_dataset(
-            data_args.dataset_name,
-            data_args.dataset_config_name,
-            cache_dir=model_args.cache_dir, 
-            streaming=True,
-            split="validation"
-        )
-        
-    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
-    # https://huggingface.co/docs/datasets/loading_datasets.html.
-
-    # Load pretrained model and tokenizer
-
-    # Distributed training:
-    # The .from_pretrained methods guarantee that only one local process can concurrently
-    # download model & vocab.
-    if model_args.config_name:
-        config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
-    elif model_args.model_name_or_path:
-        config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
-    else:
-        config = CONFIG_MAPPING[model_args.model_type]()
-        logger.warning("You are instantiating a new config instance from scratch.")
-
-    if model_args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(
-            model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
-        )
-    elif model_args.model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(
-            model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
-        )
-    else:
-        raise ValueError(
-            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
-            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
-        )
-
-    if model_args.model_name_or_path:
-        model = FlaxAutoModelForCausalLM.from_pretrained(
-            model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
-        )
-    else:
-        model = FlaxAutoModelForCausalLM.from_config(
-            config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
-        )
-
-    # Preprocessing the datasets.
-    # First we tokenize all the texts.
-    # column_names = eval_dataset.column_names
-    text_column_name = data_args.text_column_name # if data_args.text_column_name in column_names else column_names[0]
-
-    # since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
-    tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
-
-    def tokenize_function(examples):
-        with CaptureLogger(tok_logger) as cl:
-            output = tokenizer(examples[text_column_name])
-        # clm input could be much much longer than block_size
-        if "Token indices sequence length is longer than the" in cl.out:
-            tok_logger.warning(
-                "^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits before being passed to the model."
-            )
-        return output
-
-    tokenized_dataset = train_dataset.map(
-        tokenize_function,
-        batched=True,
-    )
-    tokenized_eval_dataset = eval_dataset.map(
-        tokenize_function,
-        batched=True,
-        # remove_columns=column_names,
-        # num_proc=data_args.preprocessing_num_workers,
-        # load_from_cache_file=not data_args.overwrite_cache,
-    )
-
-    if data_args.block_size is None:
-        block_size = tokenizer.model_max_length
-        if block_size > config.max_position_embeddings:
-            logger.warning(
-                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
-                "Picking 1024 instead. You can change that default value by passing --block_size xxx."
-            )
-            block_size = 1024
-    else:
-        if data_args.block_size > tokenizer.model_max_length:
-            logger.warning(
-                f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
-                f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
-            )
-        block_size = min(data_args.block_size, tokenizer.model_max_length)
-
-    # # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
-    def group_texts(examples):
-        # Concatenate all texts.
-        concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
-        total_length = len(concatenated_examples[list(examples.keys())[0]])
-        # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
-        # customize this part to your needs.
-        total_length = (total_length // block_size) * block_size
-        # Split by chunks of max_len.
-        result = {
-            k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
-            for k, t in concatenated_examples.items()
-        }
-        result["labels"] = result["input_ids"].copy()
-        return result
-
-    # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
-    # for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
-    # to preprocess.
-    #
-    # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
-    # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
-
-    shuffle_seed = training_args.seed
-    # if training_args.do_train:
-    #     if "train" not in tokenized_dataset:
-    #         raise ValueError("--do_train requires a train dataset")
-    #     train_dataset = tokenized_dataset
-    #     if data_args.max_train_samples is not None:
-    #         train_dataset = train_dataset.take(range(data_args.max_train_samples))
-    #     train_dataset = train_dataset.shuffle(buffer_size=data_args.shuffle_buffer_size, seed=shuffle_seed)
-    #     train_iter = iter(train_dataset)
-
-    
-    train_loader = PrefetchDataloader(
-        tokenized_dataset, 
-        training_args.max_steps * training_args.gradient_accumulation_steps, 
-        int(training_args.per_device_train_batch_size) * jax.device_count(),
-        block_size,
-        prefetch_buffer=data_args.prefetch_buffer,
-        seed=shuffle_seed
-    )
-    # evaluation data is not in streaming mode
-    # if training_args.do_eval:
-    #     eval_dataset = tokenized_eval_dataset.map(
-    #         group_texts,
-    #         batched=True,
-    #         num_proc=data_args.preprocessing_num_workers,
-    #         load_from_cache_file=not data_args.overwrite_cache,
-    #     )
-    #     if data_args.max_eval_samples is not None:
-    #         eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
-    
-    # Enable tensorboard only on the master node
-    has_tensorboard = is_tensorboard_available()
-    if has_tensorboard and jax.process_index() == 0:
-        try:
-            from flax.metrics.tensorboard import SummaryWriter
-
-            summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
-        except ImportError as ie:
-            has_tensorboard = False
-            logger.warning(
-                f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
-            )
-    else:
-        logger.warning(
-            "Unable to display metrics through TensorBoard because the package is not installed: "
-            "Please run pip install tensorboard to enable."
-        )
-    
-    # enable wandb tracking
-    has_wandb = find_spec("wandb") is not None 
-    if jax.process_index() == 0 and has_wandb and ("wandb" in training_args.report_to):
-        try:
-            import wandb
-            wandb.init(
-                name=training_args.run_name,
-                entity="wandb", 
-                project="hf-flax-gpt-neo-copilot",
-                sync_tensorboard=True
-            )
-            wandb.config.update(training_args)
-            wandb.config.update(model_args)
-            wandb.config.update(data_args)
-        except ImportError as e:
-            print(e)
-            has_wandb = False
-    
-
-    # Initialize our training
-    rng = jax.random.PRNGKey(training_args.seed)
-    rng, dropout_rng = jax.random.split(rng)
-
-    # Store some constant
-    num_epochs = int(training_args.num_train_epochs)
-    train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count() * training_args.gradient_accumulation_steps
-    eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
-    total_train_steps = training_args.max_steps * training_args.gradient_accumulation_steps
-
-    # Create learning rate schedule
-    gpt3_schedule_fn = gpt3_schedule(
-        training_args.warmup_steps,
-        model_args.decay_steps,
-        training_args.learning_rate,
-        training_args.learning_rate / 10.
-    )
-
-    # We use Optax's "masking" functionality to not apply weight decay
-    # to bias and LayerNorm scale parameters. decay_mask_fn returns a
-    # mask boolean with the same structure as the parameters.
-    # The mask is True for parameters that should be decayed.
-    # Note that this mask is specifically adapted for FlaxGPT2.
-    # For other models, one should correct the layer norm parameter naming
-    # accordingly.
-    def decay_mask_fn(params):
-        flat_params = traverse_util.flatten_dict(params)
-        flat_mask = {
-            path: (path[-1] != "bias" and path[-2:] not in [("ln_1", "scale"), ("ln_2", "scale"), ("ln_f", "scale")])
-            for path in flat_params
-        }
-        return traverse_util.unflatten_dict(flat_mask)
-
-    # create optimizer
-    if training_args.adafactor:
-        # We use the default parameters here to initialize adafactor,
-        # For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
-        optimizer = optax.adafactor(
-            learning_rate=gpt3_schedule_fn,
-        )
-    else:
-        optimizer = optax.adamw(
-            learning_rate=gpt3_schedule_fn,
-            b1=training_args.adam_beta1,
-            b2=training_args.adam_beta2,
-            eps=training_args.adam_epsilon,
-            weight_decay=training_args.weight_decay,
-            mask=decay_mask_fn,
-        )
-    if training_args.gradient_accumulation_steps > 1:
-        optimizer = optax.MultiSteps(optimizer, training_args.gradient_accumulation_steps)
-    grad_accum_steps = training_args.gradient_accumulation_steps
-
-    # Setup train state
-    state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer, dropout_rng=dropout_rng)
-    
-    if training_args.resume_from_checkpoint:
-        state = restore_checkpoint(training_args.resume_from_checkpoint, state)
-        resume_step = mb_item(state.step)
-    else:
-        resume_step = 0
-
-    def loss_fn(logits, labels):
-        shift_logits = logits[..., :-1, :]
-        shift_labels = labels[..., 1:]
-        loss = optax.softmax_cross_entropy(shift_logits, onehot(shift_labels, shift_logits.shape[-1]))
-        return loss.mean()
-
-    # Define gradient update step fn
-    def train_step(state, batch):
-        dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
-
-        def compute_loss(params):
-            labels = batch.pop("labels")
-            logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
-            loss = loss_fn(logits, labels)
-            return loss
-
-        grad_fn = jax.value_and_grad(compute_loss)
-        loss, grad = grad_fn(state.params)
-        grad = jax.lax.pmean(grad, "batch")
-
-        new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
-
-        metrics = {"loss": loss, "learning_rate": gpt3_schedule_fn(state.step // grad_accum_steps)}
-        metrics = jax.lax.pmean(metrics, axis_name="batch")
-
-        return new_state, metrics
-
-    # Define eval fn
-    def eval_step(params, batch):
-        labels = batch.pop("labels")
-        logits = model(**batch, params=params, train=False)[0]
-        loss = loss_fn(logits, labels)
-
-        # summarize metrics
-        metrics = {"loss": loss}
-        metrics = jax.lax.pmean(metrics, axis_name="batch")
-        return metrics
-
-    # Create parallel version of the train and eval step
-    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
-    p_eval_step = jax.pmap(eval_step, "batch")
-
-    # Replicate the train state on each device
-    state = state.replicate()
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel, distributed and grad_accum) = {train_batch_size}")
-    logger.info(f"  Total optimization steps = {training_args.max_steps}")
-
-    if not training_args.skip_memory_metrics:
-        server = jax.profiler.start_server(9999)
-
-    train_time = 0
-    train_metrics = []
-    # TODO: figure out training duration
-    steps = tqdm(range(training_args.max_steps), position=0, initial=resume_step)
-    for step in range(total_train_steps):
-        # ======================== Training ================================
-        train_start = time.time()
-        rng, input_rng = jax.random.split(rng)
-        
-        cur_step = step
-        # skip to the step from which we are resuming
-        if cur_step < resume_step:
-            continue
-        
-        # using advance_iter_and_group_samples seem to make training slower
-        # samples = advance_iter_and_group_samples(iter(tokenized_dataset), int(training_args.per_device_train_batch_size) * jax.device_count(), block_size)
-        # batch = shard(make_batch(samples))
-        batch = shard(next(train_loader))
-        # logger.info(f"{batch['input_ids'].shape}")
-        state, train_metric = p_train_step(state, batch)
-        train_metrics.append(train_metric)
-        if step % grad_accum_steps == 0:
-            steps.update(1)
-
-        if cur_step % (training_args.logging_steps * grad_accum_steps)== 0 and cur_step > 0:
-            # Save metrics
-            train_metric = unreplicate(train_metric)
-            train_time += time.time() - train_start
-            if has_tensorboard and jax.process_index() == 0:
-                write_train_metric(summary_writer, train_metrics, train_time, cur_step)
-            if has_wandb and jax.process_index() == 0 and ("wandb" in training_args.report_to):
-                # TODO: add accumulation of metrics
-                _metrics = {k if k=="learning_rate" else f"train_{k}":mb_item(v.mean()) for k, v in train_metric.items()}
-                wandb.log({"training_step":cur_step, **_metrics}, commit=True)
-
-            steps.write(
-                f"Step... ({cur_step} | Loss: {train_metric['loss'].mean()}, Learning Rate: {train_metric['learning_rate'].mean()})"
-            )
-
-            train_metrics = []
-
-        if cur_step % (training_args.eval_steps * grad_accum_steps) == 0 and cur_step > 0 and training_args.do_eval:
-            # ======================== Evaluating ==============================
-            eval_metrics = []
-            eval_steps = data_args.max_eval_samples # len(eval_dataset) // eval_batch_size
-            # eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
-            eval_loader = PrefetchDataloader(
-                tokenized_eval_dataset, 
-                eval_steps,
-                eval_batch_size,
-                block_size,
-                prefetch_buffer=data_args.prefetch_buffer,
-                shuffle=False,
-            )
-            for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
-                # Model forward
-                batch = shard(next(eval_loader))
-                metrics = p_eval_step(state.params, batch)
-                eval_metrics.append(metrics)
-
-            # normalize eval metrics
-            eval_metrics = get_metrics(eval_metrics)
-            eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
-
-            try:
-                eval_metrics["perplexity"] = math.exp(eval_metrics["loss"])
-            except OverflowError:
-                eval_metrics["perplexity"] = float("inf")
-            # TODO: this needs to be closed properly
-            eval_loader.terminate()
-            # Print metrics and update progress bar
-            desc = f"Step... ({cur_step} | Eval Loss: {eval_metrics['loss']} | Eval Perplexity: {eval_metrics['perplexity']})"
-            steps.write(desc)
-            steps.desc = desc
-
-            # Save metrics
-            if has_tensorboard and jax.process_index() == 0:
-                # cur_step = epoch * (len(train_dataset) // train_batch_size)
-                write_eval_metric(summary_writer, eval_metrics, cur_step)
-            if has_wandb and jax.process_index() == 0 and ("wandb" in training_args.report_to):
-                _metrics = {f"eval_{k}":mb_item(v) for k, v in eval_metrics.items()}
-                wandb.log({"eval_step":cur_step, **_metrics})
-
-        if cur_step % (training_args.save_steps * grad_accum_steps) == 0 and cur_step > 0:
-            # save checkpoint after each epoch and push checkpoint to the hub
-            if jax.process_index() == 0:
-                print("*********", training_args.push_to_hub)
-                save_model_checkpoint(model, training_args.output_dir, state, with_opt=False,
-                                      push_to_hub=training_args.push_to_hub)
-                if model_args.save_optimizer:
-                    # this saves full state including optimizer
-                    save_checkpoint(training_args.output_dir, jax_utils.unreplicate(state), cur_step, keep=training_args.save_total_limit, overwrite=False)
-                if training_args.save_total_limit is not None:
-                    rotate_checkpoints(training_args.output_dir, training_args.save_total_limit)
-    
-    train_loader.terminate()
-    # save model after training is over
-    save_model_checkpoint(model, training_args.output_dir, state, with_opt=False,
-                    push_to_hub=training_args.push_to_hub)
-
-
-
-
-if __name__ == "__main__":
-    main()

utils.py

@@ -1,122 +0,0 @@
-import numpy as np
-import threading
-import queue
-import multiprocessing
-from collections import defaultdict
-import jax
-import jax.numpy as jnp
-
-
-
-def make_batch(samples):
-    batch = {k:jnp.array(v) for k,v in samples.items()}
-    batch['labels'] = batch['input_ids'].copy()
-    return batch
-
-class PrefetchDataloaderTread(threading.Thread):
-    "Prefetch dataloader for IterableDataset"
-    def __init__(self, dataset, max_steps, batch_size, sequence_length, prefetch_buffer=1, shuffle=True, shuffle_buffer=1000, seed=0):
-        super().__init__(daemon=True)
-        self.max_steps = max_steps
-        self.bs = batch_size
-        self.seq_len = sequence_length
-        self.max_length = batch_size * sequence_length
-        self.prefetch_buffer = prefetch_buffer
-        self.shuffle = shuffle
-        self.shuffle_buffer = shuffle_buffer
-        self.seed = seed
-        self.dataset = dataset
-        if shuffle:
-            shuffled_dataset = dataset.shuffle(shuffle_buffer, seed=self.seed)
-            self.seed += 1
-            self.ds_iter = iter(shuffled_dataset)
-        else:
-            self.ds_iter = iter(dataset)
-        self.queue = queue.Queue(prefetch_buffer)
-        self.rem = defaultdict(list)
-        self.start()
-        
-    def __next__(self):
-        batch = self.queue.get()
-        return batch
-        
-    def run(self):
-        i = 0
-        while True and i < self.max_steps:
-            i += 1
-            # prepair next batch
-            sample = self.rem.copy()
-            l = len(sample["input_ids"])
-            max_length = self.max_length
-            while l < max_length:
-                next_sample = next(self.ds_iter)
-                l += len(next_sample["input_ids"])
-                sample = {k:sample[k]+next_sample[k] for k in next_sample.keys()}
-            
-            self.rem = {k:v[max_length:] for k,v in sample.items()}
-            sample = {k:v[:max_length] for k,v in sample.items()}
-            # regroup to shape [bs x seq_len]
-            samples = {k:np.array([v[i*self.seq_len:(i+1)*self.seq_len] for i in range(self.bs)]) for k,v in sample.items()}
-            
-            self.queue.put(make_batch(samples))
-        self.queue.put(None)
-    
-    def __iter__(self):
-        return self
-
-
-class PrefetchDataloader(multiprocessing.Process):
-    "Prefetch dataloader for IterableDataset"
-    def __init__(self, dataset, max_steps, batch_size, sequence_length, prefetch_buffer=1, shuffle=True, shuffle_buffer=1000, seed=0):
-        super().__init__(daemon=True)
-        self.max_steps = max_steps
-        self.bs = batch_size
-        self.seq_len = sequence_length
-        self.max_length = batch_size * sequence_length
-        self.prefetch_buffer = prefetch_buffer
-        self.shuffle = shuffle
-        self.shuffle_buffer = shuffle_buffer
-        self.seed = seed
-        self.dataset = dataset
-        self.make_iter()
-        self.queue = multiprocessing.Queue(prefetch_buffer)
-        self.rem = defaultdict(list)
-        self.start()
-
-    def make_iter(self):
-        if self.shuffle:
-            shuffled_dataset = self.dataset.shuffle(self.shuffle_buffer, seed=self.seed)
-            self.seed += 1
-            self.ds_iter = iter(shuffled_dataset)
-        else:
-            self.ds_iter = iter(self.dataset)
-
-    def __next__(self):
-        return make_batch(self.queue.get())
-        
-    def run(self):
-        i = 0
-        while True and i < self.max_steps:
-            # prepair next batch
-            sample = self.rem.copy()
-            l = len(sample["input_ids"])
-            max_length = self.max_length
-            while l < max_length:
-                try:
-                    next_sample = next(self.ds_iter)
-                except StopIteration:
-                    # reset generator if a pass through dataset is completed
-                    self.make_iter()
-                l += len(next_sample["input_ids"])
-                sample = {k:sample[k]+next_sample[k] for k in next_sample.keys()}
-            
-            self.rem = {k:v[max_length:] for k,v in sample.items()}
-            sample = {k:v[:max_length] for k,v in sample.items()}
-            # regroup to shape [bs x seq_len]
-            samples = {k:np.array([v[i*self.seq_len:(i+1)*self.seq_len] for i in range(self.bs)]) for k,v in sample.items()}
-            
-            self.queue.put(samples)
-        self.queue.put(None)
-    
-    def __iter__(self):
-        return self