deadline push, add new native clip output

clippy.py

@@ -0,0 +1,860 @@
+#!/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.
+
+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 jsonlines
+import shutil
+import numpy as np
+
+import datasets
+from datasets import Dataset, load_dataset
+from flax import training
+from tqdm import tqdm
+
+import torch
+from torchvision.datasets import VisionDataset
+from torchvision.io import ImageReadMode, read_image
+from torchvision.transforms import (
+    # added for image augmentation
+    ToPILImage,
+    RandomCrop,
+    ColorJitter,
+    RandomHorizontalFlip,
+    RandomVerticalFlip,
+    RandomResizedCrop,
+    ToTensor,
+    # /added for image augmentation
+    CenterCrop, 
+    ConvertImageDtype, 
+    Normalize, 
+    Resize
+)
+from torchvision.transforms.functional import InterpolationMode
+
+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,
+    AutoConfig,
+    FlaxCLIPModel,
+    CLIPProcessor,
+    CLIPTokenizerFast,
+    HfArgumentParser,
+    TrainingArguments,
+    is_tensorboard_available,
+    IntervalStrategy
+    
+)
+from transformers.testing_utils import CaptureLogger
+
+from importlib.util import find_spec
+
+logger = logging.getLogger(__name__)
+
+
+@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."
+        },
+    )
+    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"},
+    )
+
+
+@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)."}
+    )
+    data_dir: Optional[str] = field(
+        default=None, metadata={"help": "Path to local folder containing data files."}
+    )
+    train_file: Optional[str] = field(
+        default=None, metadata={"help": "The input training data file (a jsonlines file)."}
+    )
+    validation_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input evaluation data file (a jsonlines file)."},
+    )
+    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)."},
+    )
+    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."},
+    )
+    augment_images: Optional[bool] = field(
+        default=True,
+        metadata={ "help": "Augment input training images" }
+    )
+    augment_captions: Optional[bool] = field(
+        default=True,
+        metadata={ "help": "Augment input training images" }
+    )
+    captions_per_image: Optional[int] = field(
+        default=5,
+        metadata={"help": "Number of captions per image to use when creating train dataset."},
+    )
+
+    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", "jsonl"], "`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", "jsonl"], "`validation_file` should be a csv, a json or a txt file."
+
+
+@dataclass
+class ImageAugmentationArguments:
+    """
+    Arguments for image augmentations configuration
+    """
+    random_horizontal_flip: Optional[float] = field(
+        default=0.5,
+        metadata={ "help": "Probability of applying random horizontal flip" }
+    )
+    random_vertical_flip: Optional[float] = field(
+        default=0.5,
+        metadata={ "help": "Probability of applying random vartical flip" }
+    )
+
+# We use torchvision for faster image pre-processing.
+# We need to ensure faster processing speed as it can become a bottleneck on TPU
+class Transform(torch.nn.Module):
+    def __init__(self, image_size, augment_images, augmentation_args):
+        super().__init__()
+        if augment_images:
+            crop_size = int(image_size * 0.8)
+            self.transforms = torch.nn.Sequential(
+                # image augmentation transforms
+                RandomCrop(crop_size),
+                ColorJitter(),
+                RandomHorizontalFlip(augmentation_args.random_horizontal_flip),
+                RandomVerticalFlip(augmentation_args.random_vertical_flip),
+                RandomResizedCrop(crop_size, scale=(0.8, 1.2), ratio=(1.0, 1.0)),
+                # /image augmentation transforms
+                Resize([image_size], interpolation=InterpolationMode.BICUBIC),
+                CenterCrop(image_size),
+                ConvertImageDtype(torch.float),
+                Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
+            )
+        else:
+            self.transforms = torch.nn.Sequential(
+                Resize([image_size], interpolation=InterpolationMode.BICUBIC),
+                CenterCrop(image_size),
+                ConvertImageDtype(torch.float),
+                Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
+            )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        with torch.no_grad():
+            x = self.transforms(x)
+        return x
+
+class ImageTextDataset(VisionDataset):
+    """
+    Dtaset for loading image-text data for tasks like CLIP training, Image Captioning.
+
+    Args:
+        root: (string): The root path where the dataset is stored
+        file_path: (string): Path to the file containing the image_paths and associated captions.
+            The expected format is jsonlines where each line is a json object containing to keys.
+            `filename`: The path to the image.
+            `captions`: An `array` of captions.
+        transform (callable, optional): A function/transform that  takes in an PIL image
+            and returns a transformed version. E.g, ``transforms.ToTensor``
+        target_transform (callable, optional): A function/transform that takes in the
+            target and transforms it.
+        transforms (callable, optional): A function/transform that takes input sample and its target as entry
+            and returns a transformed version.
+    """
+
+    def __init__(
+        self,
+        file_path: str,
+        split: str, 
+        captions_per_image:int = 5,
+        augment_captions:bool = True,
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+        transforms: Optional[Callable] = None,
+    ):
+        super().__init__(None, transforms, transform, target_transform)
+
+        if augment_captions:
+            prefix = "textaug_"
+        else:
+            prefix = ""
+        with open(file_path, "r") as f:
+            examples = [json.loads(line) for line in f.readlines()]
+
+        self.captions = []
+        self.image_paths = []
+
+        for example in examples:
+            self.captions.extend(example["captions"][:captions_per_image])
+            self.image_paths.extend([example["image_path"]] * captions_per_image)
+    
+    def _load_image(self, idx: int):
+        path = f"{self.image_paths[idx]}"
+        return read_image(path, mode=ImageReadMode.RGB)
+
+    def _load_target(self, idx):
+        return self.captions[idx]
+
+    def __getitem__(self, index: int):
+        image = self._load_image(index)
+        target = self._load_target(index)
+
+        if self.transforms is not None:
+            image, target = self.transforms(image, target)
+
+        return image, target
+
+    def __len__(self) -> int:
+        return len(self.captions)
+
+
+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))
+
+
+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(
+    train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
+) -> Callable[[int], jnp.array]:
+    """Returns a linear warmup, linear_decay learning rate function."""
+    steps_per_epoch = train_ds_size // train_batch_size
+    num_train_steps = steps_per_epoch * num_train_epochs
+    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
+
+# utils
+def mb_item(x):
+    return x.item() if hasattr(x, "item") else x
+
+def make_batch(samples):
+    batch = {k:jnp.array(v) for k,v in samples.items()}
+    return batch
+
+#checkpoint functions
+# def save_checkpoint(model, save_dir, state, with_opt:bool=True, push_to_hub:bool=False):
+#     state = jax_utils.unreplicate(state)
+#     logger.info(f"SAVING CHECKPOINT IN {save_dir}...")
+#     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_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, ImageAugmentationArguments))
+    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, augmentation_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args, augmentation_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}")
+
+    # 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."
+    #     )
+
+    processor = CLIPProcessor.from_pretrained(model_args.model_name_or_path)
+    # tokenizer = CLIPTokenizerFast.from_pretrained(model_args.model_name_or_path)
+    tokenizer = processor.tokenizer
+    if model_args.model_name_or_path:
+        model = FlaxCLIPModel.from_pretrained(
+            model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
+        )
+    else:
+        model = FlaxCLIPModel.from_config(
+            config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
+        )
+
+    config = model.config
+    # Initialize torchvision transforms and jit them for faster processing
+    # preprocess = Transform(config.vision_config.image_size)
+    preprocess = Transform(config.vision_config.image_size, data_args.augment_images, augmentation_args)
+    preprocess = torch.jit.script(preprocess)
+
+    eval_preprocess = Transform(config.vision_config.image_size, False, augmentation_args)
+    eval_preprocess = torch.jit.script(eval_preprocess)
+
+    # Initialize the image-text dataset
+    train_dataset = ImageTextDataset(
+        data_args.train_file,
+        "train",
+        captions_per_image=data_args.captions_per_image,
+        augment_captions=data_args.augment_captions,
+        transform=preprocess,
+    )
+
+    eval_dataset = ImageTextDataset(
+        data_args.validation_file,
+        "valid",
+        captions_per_image=1,
+        augment_captions=False, 
+        transform=eval_preprocess,
+    )
+
+    # 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."
+        )
+    
+    # Use collate function to tokenizer the text and convert the processed images to numpy
+    def collate_fn(examples):
+        pixel_values = torch.stack([example[0] for example in examples]).numpy()
+        captions = [example[1] for example in examples]
+        inputs = tokenizer(captions, max_length=128, padding="max_length", return_tensors="np", truncation=True)
+
+        batch = {
+            "pixel_values": pixel_values,
+            "input_ids": inputs["input_ids"],
+            "attention_mask": inputs["attention_mask"],
+        }
+
+        return batch
+
+    # 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()
+    steps_per_epoch = len(train_dataset) // train_batch_size
+    total_train_steps = steps_per_epoch * num_epochs
+
+    # Create data loaders
+    train_loader = torch.utils.data.DataLoader(
+        train_dataset,
+        batch_size=train_batch_size,
+        shuffle=True,
+        num_workers=data_args.preprocessing_num_workers,
+        persistent_workers=True,
+        drop_last=True,
+        collate_fn=collate_fn,
+    )
+
+    eval_loader = torch.utils.data.DataLoader(
+        eval_dataset,
+        batch_size=eval_batch_size,
+        shuffle=False,
+        num_workers=data_args.preprocessing_num_workers,
+        persistent_workers=True,
+        drop_last=True,
+        collate_fn=collate_fn,
+    )
+
+    # 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
+            if training_args.run_name is None:
+                run_name = training_args.output_dir.split("/")[-1]
+            else:
+                run_name = training_args.run_name
+            wandb.init(
+                name=run_name,
+                project="clip-flax",
+                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)
+
+    # Create learning rate schedule
+    linear_decay_lr_schedule_fn = create_learning_rate_fn(
+        len(train_dataset),
+        train_batch_size,
+        training_args.num_train_epochs,
+        training_args.warmup_steps,
+        training_args.learning_rate,
+    )
+
+    # 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=linear_decay_lr_schedule_fn,
+        )
+    else:
+        optimizer = optax.adamw(
+            learning_rate=linear_decay_lr_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,
+        )
+        optimizer = optax.chain(
+            optax.clip_by_global_norm(1.),
+            optimizer
+        )
+    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 cross_entropy(logits, axis):
+        logprobs = jax.nn.log_softmax(logits, axis=axis)
+        nll = jnp.diag(logprobs)
+        ce = -jnp.mean(nll)
+        return ce
+
+    def clip_loss(similarity):
+        loss = (cross_entropy(similarity, axis=0) + cross_entropy(similarity, axis=1)) / 2
+        return loss
+
+    # 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):
+            logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
+            loss = clip_loss(logits)
+            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": linear_decay_lr_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):
+        logits = model(**batch, params=params, train=False)[0]
+        loss = clip_loss(logits)
+
+        # 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"  Num examples = {len(train_dataset)}")
+    logger.info(f"  Num Epochs = {num_epochs}")
+    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 = {total_train_steps}")
+
+    if not training_args.skip_memory_metrics:
+        server = jax.profiler.start_server(9999)
+
+    train_time = 0
+    train_metrics = []
+    epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
+    for epoch in epochs:
+        # ======================== Training ================================
+        train_start = time.time()
+
+        # Create sampling rng
+        rng, input_rng = jax.random.split(rng)
+
+        # Generate an epoch by shuffling sampling indices from the train dataset
+        steps_per_epoch = len(train_dataset) // train_batch_size
+        # train
+        steps_trained_progress_bar = tqdm(range(steps_per_epoch), desc="Training...", position=1,
+                                          leave=False, initial=(resume_step // grad_accum_steps))
+        for step, batch in enumerate(train_loader):
+            cur_step = epoch * (len(train_dataset) // train_batch_size) + step
+            # skip to the step from which we are resuming
+            if cur_step < resume_step:
+                continue
+            
+            batch = shard(make_batch(batch))
+            
+            state, train_metric = p_train_step(state, batch)
+            train_metrics.append(train_metric)
+            if step % grad_accum_steps == 0:
+                steps_trained_progress_bar.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)
+
+                epochs.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 
+            #     model_args.eval_strategy == "steps"):
+            #     # ======================== Evaluating ==============================
+            #     eval_metrics = []
+            #     eval_steps = len(eval_dataset) // eval_batch_size
+            #     eval_iter = iter(eval_loader)
+            #     for batch in tqdm(eval_loader, desc="Evaluating...", position=2, leave=False):
+            #         # Model forward
+            #         batch = shard(make_batch(batch))
+            #         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)
+
+            #     # Print metrics and update progress bar
+            #     desc = f"Step... ({cur_step} | Eval Loss: {eval_metrics['loss']})"
+            #     epochs.write(desc)
+            #     epochs.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})
+
+        # we can add an argument to select eval strategy; for now its done every epoch
+        if True:
+            # ======================== Evaluating ==============================
+            eval_metrics = []
+            eval_steps = len(eval_dataset) // eval_batch_size
+            for batch in tqdm(eval_loader, desc="Evaluating...", position=2, leave=False):
+                # Model forward
+                batch = shard(make_batch(batch))
+                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)
+
+            # Print metrics and update progress bar
+            desc = f"Step... ({cur_step} | Eval Loss: {eval_metrics['loss']})"
+            epochs.write(desc)
+            epochs.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})
+
+        # save checkpoint after each epoch
+        if jax.process_index() == 0 and training_args.save_strategy == IntervalStrategy.EPOCH:
+            save_dir = f"{training_args.output_dir}/ckpt-{epoch}"
+            model.save_pretrained(
+                save_dir,
+                params=unreplicate(state.params),
+                push_to_hub=False, # training_args.push_to_hub, # we don't push intermediate steps
+                commit_message=f"Saving weights and logs at epoch {epoch}",
+                repo_name_or_path=training_args.output_dir
+            )
+            if model_args.save_optimizer:
+                save_checkpoint(training_args.output_dir, unreplicate(state), cur_step, keep=training_args.save_total_limit, overwrite=True)
+            if training_args.save_total_limit is not None:
+                rotate_checkpoints(training_args.output_dir, training_args.save_total_limit)
+    
+    # save model after training is over
+    model.save_pretrained(
+        training_args.output_dir,
+        params=unreplicate(state.params),
+        push_to_hub=training_args.push_to_hub,
+        commit_message=f"Saving weights and logs at step {cur_step}",
+        repo_name_or_path=training_args.output_dir
+    )
+
+
+
+
+if __name__ == "__main__":
+    main()

configuration_hybrid_clip.py

@@ -0,0 +1 @@
+/home/ceyda/code/transformers/examples/research_projects/jax-projects/hybrid_clip/configuration_hybrid_clip.py

convert_model.ipynb

@@ -0,0 +1,62 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "from modeling_hybrid_clip import FlaxHybridCLIP\n",
+    "model = FlaxHybridCLIP.from_pretrained(\"output\")\n",
+    "# model.save_pretrained(\"./\")"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "source": [
+    "from transformers.modeling_flax_pytorch_utils import load_flax_checkpoint_in_pytorch_model\n",
+    "from modeling_hybrid_clip import FlaxHybridCLIP\n",
+    "\n",
+    "model = FlaxHybridCLIP.from_pretrained(\"output\")\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "load_flax_checkpoint_in_pytorch_model(model, \"./flax_model.msgpack\")\n",
+    "model.save_pretrained(\"./\")"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "model.save_pretrained(\"./test\",)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "tcmalloc: large alloc 1284136960 bytes == 0x133cb8000 @  0x7f0ad1bd7680 0x7f0ad1bf7bdd 0x7f07e8e4420d 0x7f07e8e52340 0x7f07e8e51e87 0x7f07e8e51e87 0x7f07e8e51e87 0x7f07e8e51e87 0x7f07e8e51e87 0x7f07e8e51e87 0x7f07e8e51e87 0x7f07e8e51e87 0x7f07e8e4dbd3 0x7f07e8e4e1fe 0x504d56 0x56acb6 0x568d9a 0x5f5b33 0x56bc9b 0x5f5956 0x56aadf 0x5f5956 0x56aadf 0x568d9a 0x5f5b33 0x56acb6 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

modeling_hybrid_clip.py

@@ -0,0 +1 @@
+/home/ceyda/code/transformers/examples/research_projects/jax-projects/hybrid_clip/modeling_hybrid_clip.py

out_clip/ckpt-0/config.json

@@ -0,0 +1,153 @@
+{
+  "architectures": [
+    "CLIPModel"
+  ],
+  "initializer_factor": 1.0,
+  "model_type": "clip",
+  "projection_dim": 512,
+  "text_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
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+    "forced_eos_token_id": null,
+    "gradient_checkpointing": false,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 512,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 2048,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
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+    "min_length": 0,
+    "model_type": "clip_text_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 8,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
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+    "output_scores": false,
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+    "pruned_heads": {},
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+  "transformers_version": null,
+  "vision_config": {
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+    "architectures": null,
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+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
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+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
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+    "is_encoder_decoder": false,
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+    "use_bfloat16": false
+  },
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out_clip/ckpt-0/flax_model.msgpack

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+size 605123003

out_clip/ckpt-1/config.json

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+  "architectures": [
+    "CLIPModel"
+  ],
+  "initializer_factor": 1.0,
+  "model_type": "clip",
+  "projection_dim": 512,
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+#! /bin/bash
+MODEL_DIR=./out_clip
+BASE_V_MODEL=openai/clip-vit-base-patch32
+python clippy.py \
+    --output_dir ${MODEL_DIR} \
+    --model_name_or_path ${BASE_V_MODEL} \
+    --train_file="/home/ceyda/data/train.json" \
+    --validation_file="/home/ceyda/data/val.json"  \
+    --do_train --do_eval \
+    --per_device_train_batch_size="32" \
+    --per_device_eval_batch_size="8" \
+    --preprocessing_num_workers="16" \
+    --learning_rate="1e-5" \
+    --adafactor false \
+    --warmup_steps="50" \
+    --adam_beta1="0.9" \
+    --adam_beta2="0.98" \
+    --weight_decay="0.1" \
+    --overwrite_output_dir \
+    --num_train_epochs 10 \
+    --logging_steps="2" \
+    --eval_steps="20" \
+    --push_to_hub="False" \
+    --dtype="bfloat16" \
+    --skip_memory_metrics="True" \
+    --save_steps="200" \
+    --save_total_limit 10 \
+    --gradient_accumulation_steps 1 \
+    --report_to all \
+    --save_strategy epoch \
+    --save_optimizer="False" \
+    --captions_per_image 1 \
+    --augment_images false \
+    --augment_captions false
+    # --run_name="test_run" \
+    # --resume_from_checkpoint $HOME/gpt-neo-125M-code-clippy/ckpt_201 \
+    # --max_train_samples 10240 \
+    # --max_eval_samples="1000"